{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 特征工程的神器-基于Python的特征自动化选择代码\n",
    ">本文介绍一个特征选择神器：特征选择器是用于减少机器学习数据集的维数的工具，可以傻瓜式地进行特征选择。\n",
    ">\n",
    ">来源：[Will Koehrsen](https://github.com/WillKoehrsen)\n",
    ">\n",
    ">代码整理及注释翻译：[黄海广](https://github.com/fengdu78)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实现的功能\n",
    "该选择器基于python编写，有五种方法来标识要删除的特征：\n",
    "\n",
    "- 缺失值\n",
    "- 唯一值\n",
    "- 共线特征\n",
    "- 零重要性特征\n",
    "- 低重要性特征"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用方法\n",
    "\n",
    "\n",
    "在这个Jupyter文件中， 我们将使用 `FeatureSelector` 类来选择数据集中要删除的特征，这个类提供五种方法来查找要删除的功能：  \n",
    "\n",
    "1. 查找缺失分数大于指定阈值的列\n",
    "2. 查找只有唯一值的特征\n",
    "3. 查找由相关系数大于指定值的共线特征\n",
    "4. 使用梯度增强算法查找具有零重要性的特征\n",
    "5. 使用梯度增强算法查找中查找对指定的累积特征重要性无贡献的特征 \n",
    "\n",
    " `FeatureSelector` 仍在进一步开发中! 欢迎大家在github提交PR."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from feature_selector import FeatureSelector\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 示例数据集\n",
    "\n",
    "该数据集被用作Kaggle上房屋信用违约风险竞赛的(https://www.kaggle.com/c/home-credit-default-risk)  一部分(数据放在`data`目录里)。它旨在用于有监督的机器学习分类任务，其目的是预测客户是否会拖欠贷款。您可以在此处下载整个数据集，我们将处理10,000行的一小部分样本。\n",
    "\n",
    "特征选择器旨在用于机器学习任务，但可以应用于任何数据集。基于特征重要性的方法需要使用机器学习的监督学习问题。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SK_ID_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>247408</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>N</td>\n",
       "      <td>2</td>\n",
       "      <td>108000.0</td>\n",
       "      <td>172512.0</td>\n",
       "      <td>13477.5</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>153916</td>\n",
       "      <td>0</td>\n",
       "      <td>Revolving loans</td>\n",
       "      <td>F</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>2</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>9000.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>229065</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>112500.0</td>\n",
       "      <td>463500.0</td>\n",
       "      <td>20547.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>282013</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>549882.0</td>\n",
       "      <td>17739.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>142266</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>90000.0</td>\n",
       "      <td>518562.0</td>\n",
       "      <td>20695.5</td>\n",
       "      <td>...</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
       "0      247408       0         Cash loans           F            Y   \n",
       "1      153916       0    Revolving loans           F            Y   \n",
       "2      229065       0         Cash loans           F            N   \n",
       "3      282013       0         Cash loans           F            N   \n",
       "4      142266       0         Cash loans           F            N   \n",
       "\n",
       "  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
       "0               N             2          108000.0    172512.0      13477.5   \n",
       "1               Y             2          135000.0    180000.0       9000.0   \n",
       "2               Y             0          112500.0    463500.0      20547.0   \n",
       "3               Y             0          135000.0    549882.0      17739.0   \n",
       "4               Y             0           90000.0    518562.0      20695.5   \n",
       "\n",
       "              ...              FLAG_DOCUMENT_18 FLAG_DOCUMENT_19  \\\n",
       "0             ...                             0                0   \n",
       "1             ...                             0                0   \n",
       "2             ...                             0                0   \n",
       "3             ...                             0                0   \n",
       "4             ...                             0                0   \n",
       "\n",
       "  FLAG_DOCUMENT_20 FLAG_DOCUMENT_21 AMT_REQ_CREDIT_BUREAU_HOUR  \\\n",
       "0                0                0                        0.0   \n",
       "1                0                0                        0.0   \n",
       "2                0                0                        0.0   \n",
       "3                0                0                        0.0   \n",
       "4                0                0                        0.0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_DAY  AMT_REQ_CREDIT_BUREAU_WEEK  \\\n",
       "0                       0.0                         0.0   \n",
       "1                       0.0                         0.0   \n",
       "2                       0.0                         1.0   \n",
       "3                       0.0                         0.0   \n",
       "4                       0.0                         0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_MON  AMT_REQ_CREDIT_BUREAU_QRT  \\\n",
       "0                        0.0                        0.0   \n",
       "1                        0.0                        0.0   \n",
       "2                        0.0                        0.0   \n",
       "3                        0.0                        0.0   \n",
       "4                        0.0                        1.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                         1.0  \n",
       "1                         0.0  \n",
       "2                         7.0  \n",
       "3                         1.0  \n",
       "4                         1.0  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('data/credit_example.csv')\n",
    "train_labels = train['TARGET']\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据集中有几个分类列。`FeatureSelector`处理这些特征重要性的时候使用独热编码。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = train.drop(columns = ['TARGET'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 实施\n",
    "\n",
    "在`FeatureSelector`具有用于标识列，以除去五种特征 :\n",
    "\n",
    "* `identify_missing`（查找缺失值）\n",
    "* `identify_single_unique`（查找唯一值）\n",
    "* `identify_collinear`（查找共线特征）\n",
    "* `identify_zero_importance` （查找零重要特征）\n",
    "* `identify_low_importance`（查找低重要特征）\n",
    "\n",
    "这些方法找到要根据指定条件删除的特征。 标识的特征存储在 `FeatureSelector`的 `ops` 属性（Python词典）中。 我们可以手动删除已识别的特征，也可以使用 `FeatureSelector`中的删除特征函数真正删除特征。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 创建实例\n",
    "\n",
    "`FeatureSelector` 仅需要一个在行中具有观察值而在列中具有特征的数据集（标准结构化数据）。 我们正在处理机器学习的分类问题，因此我们也需要训练的标签。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "fs = FeatureSelector(data = train, labels = train_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. 缺失值\n",
    "\n",
    "第一种特征选择方法很简单：找到丢失分数大于指定阈值的任何列。 在此示例中，我们将使用阈值0.6，这对应于查找缺失值超过60％的特征。 （此方法不会首先对特征进行一次独热编码）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17 features with greater than 0.60 missing values.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs.identify_missing(missing_threshold=0.6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 可以通过 `FeatureSelector` 对象的`ops`词典访问已确定要删除的特征。  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['OWN_CAR_AGE',\n",
       " 'YEARS_BUILD_AVG',\n",
       " 'COMMONAREA_AVG',\n",
       " 'FLOORSMIN_AVG',\n",
       " 'LIVINGAPARTMENTS_AVG',\n",
       " 'NONLIVINGAPARTMENTS_AVG',\n",
       " 'YEARS_BUILD_MODE',\n",
       " 'COMMONAREA_MODE',\n",
       " 'FLOORSMIN_MODE',\n",
       " 'LIVINGAPARTMENTS_MODE']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_features = fs.ops['missing']\n",
    "missing_features[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们还可以绘制数据集中所有列的缺失列分数的直方图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x148c63a3630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs.plot_missing()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有关缺失分数的详细信息，我们可以访问`missing_stats`属性，这是所有特征缺失分数的DataFrame。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>missing_fraction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_AVG</th>\n",
       "      <td>0.6953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_MODE</th>\n",
       "      <td>0.6953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COMMONAREA_MEDI</th>\n",
       "      <td>0.6953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_AVG</th>\n",
       "      <td>0.6945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_MODE</th>\n",
       "      <td>0.6945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NONLIVINGAPARTMENTS_MEDI</th>\n",
       "      <td>0.6945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_MEDI</th>\n",
       "      <td>0.6846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_AVG</th>\n",
       "      <td>0.6846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LIVINGAPARTMENTS_MODE</th>\n",
       "      <td>0.6846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FONDKAPREMONT_MODE</th>\n",
       "      <td>0.6820</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          missing_fraction\n",
       "COMMONAREA_AVG                      0.6953\n",
       "COMMONAREA_MODE                     0.6953\n",
       "COMMONAREA_MEDI                     0.6953\n",
       "NONLIVINGAPARTMENTS_AVG             0.6945\n",
       "NONLIVINGAPARTMENTS_MODE            0.6945\n",
       "NONLIVINGAPARTMENTS_MEDI            0.6945\n",
       "LIVINGAPARTMENTS_MEDI               0.6846\n",
       "LIVINGAPARTMENTS_AVG                0.6846\n",
       "LIVINGAPARTMENTS_MODE               0.6846\n",
       "FONDKAPREMONT_MODE                  0.6820"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.missing_stats.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. 唯一值\n",
    "\n",
    "下一个方法很简单：找到只有一个唯一值的所有特征。 （这不会对特征进行独热编码）。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 features with a single unique value.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs.identify_single_unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['FLAG_MOBIL', 'FLAG_DOCUMENT_10', 'FLAG_DOCUMENT_12', 'FLAG_DOCUMENT_17']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_unique = fs.ops['single_unique']\n",
    "single_unique"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们可以绘制数据集中每个特征中唯一值数量的直方图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x148c75f01d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs.plot_unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最后，我们可以访问一个DataFrame，其中包含每个特征的唯一值数量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nunique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REG_CITY_NOT_LIVE_CITY</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLAG_DOCUMENT_10</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENTRANCES_MEDI</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LANDAREA_AVG</th>\n",
       "      <td>1477</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        nunique\n",
       "CODE_GENDER                   3\n",
       "REG_CITY_NOT_LIVE_CITY        2\n",
       "FLAG_DOCUMENT_10              1\n",
       "ENTRANCES_MEDI               41\n",
       "LANDAREA_AVG               1477"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.unique_stats.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. 共线(高相关性) 特征\n",
    "\n",
    "该方法基于皮尔森相关系数找到共线特征对。 对于高于指定阈值（就绝对值而言）的每一对，它标识要删除的变量之一。 我们需要传递一个 `correlation_threshold`。\n",
    "\n",
    "此方法基于在：https://chrisalbon.com/machine_learning/feature_selection/drop_highly_correlated_features/ 中找到的代码。\n",
    "\n",
    "对于每一对，将要删除的特征是在DataFrame中列排序方面排在最后的特征。 （除非`one_hot = True`，否则此方法不会预先对数据进行一次独热编码。因此，仅在数字列之间计算相关性）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24 features with a correlation magnitude greater than 0.97.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs.identify_collinear(correlation_threshold=0.975)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AMT_GOODS_PRICE',\n",
       " 'FLAG_EMP_PHONE',\n",
       " 'YEARS_BUILD_MODE',\n",
       " 'COMMONAREA_MODE',\n",
       " 'ELEVATORS_MODE']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlated_features = fs.ops['collinear']\n",
    "correlated_features[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们可以查看阈值以上相关性的热图。 将要删除的特征位于x轴上。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x148c78018d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs.plot_collinear()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "要绘制数据中的所有相关性，我们可以将 `plot_all = True` 传递给 `plot_collinear`函数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x148c77f8f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs.plot_collinear(plot_all=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21 features with a correlation magnitude greater than 0.98.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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//vorlStXfuR2z507xy+//ELz5s2pW7cuQ4YMITU1lTlz5hAWFsaWLVuMh1zcKzExkb179+Lk5MTs2bNzTTYmTpzI4cOHOXnyJPXq1TP25fb0wLNnzwL/G3GrVq0a58+fz3UENvtzv/uce/Towdy5c4mMjOTChQukp6dbTf/L/rzS0tJyfKYAO3fupFy5cg+dYNlK9r3o5uaW47M/d+4cP//8M46OjlgsFn766SfS0tJo3LgxjRs3ZvTo0SQlJfH3v/+dmJgY9u/fT/v27R869t333b0sFgtnz56lbNmylCpVKsf+8uXLU7p0aeNzvdv58+cfug8iIn+E1rSJiOSTFi1a4O3tTVRUFCtXrsyxPykpibFjx2I2mxk1ahQODg489dRTVK9enc8//5wzZ85Y1d+wYQNxcXH4+Pj87j41bNiQqlWrsnHjRmOKWLb169fz6quvGqNM97pz5w4pKSlUq1bNKmEzm82sWLHC+HO27JGd+40+eHt7U7RoUZYtW2ZMQYSs/zgvWLAAwHjU/qN4++23GTBgAJcuXTLKihcvbqw3u18Cs2nTJjIyMvD398fb25suXbpY/fj6+hojbPc+/j86OtoqcUtNTSUkJIRixYoZI5u+vr4kJyfzn//8x+rYq1evEhYWhpOTk9UaxTJlyuDt7U10dDRbtmyhZs2atGjRwtjv5uZGkyZN2LVrF0ePHrVqMyYmhpEjR+b62oCC5uvrC2Stk7x7jWZaWhqTJ08mODiYa9euYWdnx8iRIxkxYoTVax2cnJyMV0ncb3Q4N9nfg02bNhnTNLOFh4dz8eJFo3/3KlKkCF27duX06dM51gqGhIQ8Uj9ERH4vjbSJiOSjWbNmcevWLebMmcPmzZvx8fHBxcWFn3/+mY0bN3L9+nX69u1L3759gaxkYsaMGQwdOpSXX36ZwMBAqlWrxrFjx9i4cSNVq1Zl/Pjxv7s/2e0PGzaMgIAAevfuTY0aNfjhhx/YsGEDNWrUYMSIEbkeW7ZsWVq0aMHevXuZPHkyTZs25ebNm2zevJmzZ89SpEgRkpKSjPrZ64o+//xzLBZLjqmEkDXi8eqrrzJv3jz8/f0JCAjA0dGRL7/8kq+//pqOHTvy3HPPPfJ5Dh8+nIMHD9KnTx969uyJq6sr58+fJywsjMqVK/Pss8/meWz2O+QCAwPzrBMUFMS6devYvHmz1bTWkiVL0qdPH0wmEw4ODkRERPDTTz8xZcoU3NzcABg6dCi7du1i4cKFnDx5klatWvHLL7+wbt06kpOTeffdd401WNlefPFFtmzZwrfffpvj8faQ9cCVvn37YjKZ6NmzJ08++SSxsbGEh4fj4uJivG6gMGnXrh0vvPACGzdupHfv3vj5+WFvb89nn33G8ePHMZlMPPXUUwCMGjWKiRMn0rt3b/z9/SlbtiwnTpwgPDycBg0a0LJly0eKbW9vb3wPXnrpJatXa3z22WdUr1491+ucbdy4cezbt49x48bRu3dvatWqxe7duzl+/PgfuiYiIg9LSZuISD4qU6YMISEhREZGEhERQVhYGNevX6d06dL87W9/o2/fvjmmdbVq1Yrw8HCWLFnChg0bSE5OpkqVKgwcOJDg4ODfPTUy2zPPPEN4eDhLly5lw4YNJCUlUalSJfr06cOwYcNyPLzkbvPnz2fevHns3buXLVu24OrqSsOGDXnnnXeYNm0a3377Lb/99hslS5akVatW9OjRg507d/LDDz/QvHnzXNscOnQotWvXZtWqVSxbtgyAWrVq8Y9//IPAwMDftRarRYsWRnthYWHcvHmTChUq0L17d0aOHEnZsmVzPe67777j3LlztGrVKsfTO+9Wu3ZtvLy82LVrF5s3bzYeANK7d2/s7e1Zs2YNN27c4Mknn2TBggVWo4WlSpUiLCyM5cuXs23bNmJiYnBycqJ58+YMGTKExo0b54jXunVrqlatyuXLl3nhhRdy7K9Xrx4REREsWbKEL774gnXr1uHq6sqzzz7LiBEj8pzyWtBmz56Np6cn4eHhLFiwAHt7e2rVqsWsWbMICAgw6vn7+1O6dGlWrVpFSEgISUlJVKlShf79+xMcHPy7pn7e/T345JNPSElJoUqVKgwePJhhw4bh5OSU57Hly5fnk08+4b333iMyMpJff/0VT09PPvzww1w/HxGR/GZnedRHf4mIiIiIiIjNaE2biIiIiIhIIaakTUREREREpBBT0iYiIiIiIlKIKWkTEREREREpxJS0iYiIiIiIFGJK2kRERERERAoxJW0iIiIiIiKFmF6uLQUmPf6iTeMVq1YVgKvzl9o0ruurw/ll2Uqbxiw/7BUAknfvs2nc0l5tuPHxBpvGLBf4IgDn+wy2adyaYR9yY224TWOW69sTgNuR220at8yzPqRfvGzTmMWqVgYokLgF9X1NSkqyaVwnJ6cCuZcALk9926ZxK898k597vmLTmABPhK/k6oIPbBrTdWywze8lyLqfTrd/1qYxn9wTafN/0yHr3/WCuIcB4gaOsmncGisW2zSe5E0jbSIiIiIiIoWYkjYREREREZFCTEmbiIiIiIhIIaakTUREREREpBBT0iYiIiIiIlKIKWkTEREREREpxJS0iYiIiIiIFGJK2kRERERERAoxJW0iIiIiIiKFmJI2ERERERGRQsy+oDvwVxIREcHy5ctxdXUFoGzZsty6dYvQ0FCjzjfffMPkyZOpWrWqUTZ16lSWLl3KggULjLKePXuydu1aihUrRlBQED4+PvTr14/Y2FimT5/O7du3SUpKomrVqvTs2RNfX19mz55NbGwsZrOZoKAg/Pz8rPpkNptxdHRk6dKlFClShBkzZhAXF4fFYmHMmDF4enrmOKf4+Hg6d+7Mp59+SqNGjQB46623SE1NZc6cOcTGxjJ79mzS0tIoWbIk06dPp3LlyphMJgAsFgv29vZMnz6dmjVrGuXZunfvTq9evfLvQxARERERecwoaXtEQ4cOJSAgwNi+N0kBeOGFFxg9erRVWXx8PGlpaTg4OBAbG0udOnUoVqwYV69exc3NjZiYGPr160fdunUJDQ3lm2++4eDBg0Y7CxcupFGjRrz11lukpaUxatQo3N3dc/Rp0aJFHDx4EDs7OypWrMi0adNITk5m4MCBhIeH53pO1atXZ/fu3UbSdurUKZ544gnMZjPTpk1j3rx5uLm5ERsby9SpU1mxYgWAkaz+/PPPvPvuuyxatMiqXERERERE/jhNj7SRtm3bcvDgQQB27NhB165dAdi+fTteXl44OTlx4cKFPI8/dOiQkZg5ODgQGBhIZGRkjnoJCQkUK1YMJycn9uzZw3fffYejoyNr1qzJs21PT0+OHDkCwMmTJ6lXrx4AR48epVmzZri5uQFQt25dSpcuTXx8vNXxTzzxBOnp6WRkZDzs5RARERERkYekpO0RLV++HJPJhMlkYuHChbnW2bhxo1HnH//4BwBdu3YlOjoayErAnnnmGQCio6Pp0KEDXbp0yTUJy0uNGjVITEw0+tSrVy/atWtH1apVadmyJY0bN6Z///7MmzeP9u3bs2nTpjzbKlq0KOXKleP69evExMTQsWNHAK5cuULlypWt6lavXt2Ie7dSpUpx48YNAOPcTSYThw8ffuhzEhERERGRnDQ98hH93umR9evX58yZM1y6dIlKlSpRrFgxrl+/zsmTJxk7dixms5nbt28zbNiwXOOazWar7StXrhhr67L7NGPGDBwdHQGIjY2lbdu2+Pn5kZCQwODBg3nuuecoUaJEru23b9+ePXv2cObMGXr06AGAi4sLsbGxeca92y+//IKzszOg6ZEiIiIiIvlJI2021LhxY5YtW2Y1NXLkyJGEhISwevVqKleuzKlTp3I91tPTkw0bNgCQlpbGihUrjHayjR8/nvXr15OYmMjevXv5+OOPAahQoQLOzs5kZmbm2bf27duzZcsWKlSoYJQ1a9aMffv2kZCQAGStdUtMTKR69epWx547d46SJUtib6/fAYiIiIiI5Df9L/sRLV++nI0bNxrbiYmJxshbrVq16NmzJxs3bjTWrwHMmjWL6tWr07VrV4YMGcIbb7wBZK1tmzVrllHPx8eHLVu24OHhkSPu2LFjmTFjBp988gmZmZkEBQXh7u7O8ePHjTolSpRg6NChvP/++0ydOpXp06fz4osv4ujoSGBgoDEKl5uyZcvy22+/4eXlZZQ5ODjwz3/+k7///e+kpaVRunRpZs+ebew3mUxYLBYcHByYPn26VXm2Fi1aMGbMmPtfVBERERERyZOStkcQEBBgNTUyL9lr1+7VoEED9u/fb2yHhIRY7ff39zf+3LJlS1q2bGlsFy9enLfffjvXPt3bRnY7M2fOfGBfq1Wrxpw5cwBYu3atUZ5dVq9ePT766KMcx+U1BVJTI0VERERE8peStsdIRESE1SghQHBwMG3atCmgHomIiIiIyIMoaXuMPOxIoYiIiIiIFB56EImIiIiIiEghpqRNRERERESkEFPSJiIiIiIiUogpaRMRERERESnE7CwWi6WgOyEiIiIiIiK500ibiIiIiIhIIaZH/kuBSUpKsmk8JyenrLhf7rZt3M5enG7ra9OYT+6NAuDOiVM2jVuivkeBfa431obbNG65vj357YcfbRqzZKMGANza/IVN45bt0ZX0Kwk2jVmskhsAd07+ZNO4Jeq5F9j3tSC+O7c+j7RpzLLPPQtA/JiJNo1bbeFcrq9YY9OYAC4D+5EcvcemMUt3am/zewmy7qdrH6ywacwKwQNJOfS9TWMCOLZoWiD3MMD11R/bNK5L/0CbxpO8aaRNRERERESkEFPSJiIiIiIiUogpaRMRERERESnElLSJiIiIiIgUYkraRERERERECjElbSIiIiIiIoWYkjYREREREZFCTEmbiIiIiIhIIaakTUREREREpBCzL+gO/JVERESwfPlyXF1dAfDy8sLFxYWAgACjTlpaGrNnzyY2Nhaz2UxQUBB+fn4AbN++ndWrVwNQp04d3njjDRwcHGjevDn169cH4NatW7z11ls0a9aMnTt3EhoaStGiRfH09GT06NFEREQwa9Ysvv76a+ztsz4+Pz8/hgwZQtWqVTl48CAvvPACL7/8Mtu3b8fJyYn4+HgWL17MnDlzcpzTokWLiIqKYsuWLQCkp6fTqlUrlixZQsuWLVm7di2RkZFkZGTQqlUrxo4dy8WLF+nVqxe1a9cmPT2dBg0a8OabbxIfH2+UZ5s7dy5VqlT5Ez4NEREREZHHg0baHtHQoUMJDQ0lNDQUFxeXHPs/+OADGjVqRGhoKKtXr2bjxo2cOXOG8+fP8+mnn7Jy5UrWrl2Lp6cnixcvBqB+/fpGm0uWLCEsLAyA1atXs3LlSlasWMHly5c5fPgwAM7Oznz//fcAXLhwgZSUlBz9yMzMNNp/kDt37nDx4kUAvv32W0qXLg3AN998w/HjxwkNDWXdunXY29sTHh4OQLt27YxyFxcXIiMjrcqzf5SwiYiIiIj8MUra8tmhQ4eMkTcHBwcCAwOJjIwkMjKSAQMG4ODgAMALL7zA3r17cxyfkJBAsWLFAEhJSeHzzz8nKSmJ6dOn06RJEwA6dOjA7t27AYiOjqZjx4452unQoQNHjx7lzJkzD+zz3e3t3r0bLy8vADZv3kxwcDBFimTdJgMGDDBG5O728ssv53ouIiIiIiLyxylpe0TLly/HZDJhMpmwWCwPrF+jRg0SExO5fPkylSpVstpXokQJAE6cOIHJZKJTp04sX76c1157DYD58+ezb98+fH19mTBhAnfu3AHgqaee4uTJkwAcP36cRo0a5YhrZ2fHpEmTmDt37gP72KZNG77++msArl27Zkz/vHz5MpUrVzbqlS5dmoyMjBzHOzs7k5iYCMBXX31lXJ/hw4c/MLaIiIiIiNyf1rQ9oqFDhxojaRERETn2m81mq+0rV67g6upKZmYmCQkJVuu9MjMzgf9Njzx8+DBz586lfPnyJCcnc+fOHf71r39hNpuZM2cOkZGR2NnZAVC1alV++uknSpUqZZTdq0mTJpQtW9YYRcuLo6MjGRkZnDhxgtq1axuJmYuLCwkJCVSvXh3ImkaZPQp4t6tXr1K+fHkga3pkbmvnREREREResdYgAAAgAElEQVTk99FIWz7z9PRkw4YNQNZDSVasWEHXrl3x8/NjxYoVpKWlAfDJJ5/QvHnzHMc2bdqUsLAw0tLSmDx5Mr/99hv29vbUqlXLSPIg6yEoc+fOpW3btvftz/jx4/nwww8f2O+WLVvy7rvv0qlTJ6Ose/fuLFmyxIi7dOlSunbtmuPYDRs2GFMqRUREREQkfylp+4MWL15MQEAAAQEBHDx4kLFjx3LkyBF69+5Nv379eOGFF3B3d8fDwwN/f39MJhO9e/fm6NGjjB49Okd7wcHBfPzxxzg4ODB48GACAwMxmUycPHkSf39/o94zzzzDsWPHaNOmzX375+bmRq9evR54Hh07duTMmTPUq1fPKPPy8qJevXr07t2bnj17kpmZabSVPQ2yT58+JCUlGU/IvHt6pMlkMh6eIiIiIiIiv4+d5WEWZon8CZKSkmwaz8nJKSvul/efLprvcTt7cbqtr01jPrk3CoA7J07ZNG6J+h4F9rneWBtu07jl+vbktx9+tGnMko0aAHBr8xc2jVu2R1fSryTYNGaxSm4A3Dn5k03jlqjnXmDf14L47tz6PNKmMcs+9ywA8WMm2jRutYVzub5ijU1jArgM7Edy9B6bxizdqb3N7yXIup+ufbDCpjErBA8k5dD3No0J4NiiaYHcwwDXV39s07gu/QNtGk/ypjVtj5HXXnuNa9euGdtFihQx3hsnIiIiIiKFk5K2x8h7771X0F0QEREREZFHpDVtIiIiIiIihZiSNhERERERkUJMSZuIiIiIiEghpqRNRERERESkEFPSJiIiIiIiUojpPW0iIiIiIiKFmB75LwUmLS7epvEcalQD4Op779s0rutrIwvkhaMAybv22jRu6Q5tufHxBpvGLBf4IgA/9x5o07hPrFvB9dD1No3pYuoFFNDLtS9etmnMYlUrAxRI3GtLQmwas8KIQQBcT7lj07gujiW4vSXKpjHLdM96cfmliW/ZNG6VudM591KQTWMC1Pr0I64u+MCmMV3HBhfYy7UL4sX0tv43HbL+Xb80aZpNY1aZkxXvfFCwTePW/Mi296/kTdMjRURERERECjElbSIiIiIiIoWYkjYREREREZFCTEmbiIiIiIhIIaakTUREREREpBBT0iYiIiIiIlKIKWkTEREREREpxJS0iYiIiIiIFGJK2kRERERERAox+4LuwP8VERERLF++HFdXVwDKli3LrVu3CA0NNep88803TJ48mapVqxplU6dOZenSpSxYsMAo69mzJ2vXrqVYsWIEBQXh4+NDv379iI2NZfr06dy+fZukpCSqVq1Kz5498fX1Zfbs2cTGxmI2mwkKCsLPz8+qT2azGUdHR5YuXUqRIkWYMWMGcXFxWCwWxowZg6enZ57nNnnyZJydnZk4cSI3b94kODiYdevWGfuHDBnC7NmzMZvNTJs2jVu3bmFnZ8egQYPo3Llzfl5mEREREZHHjpK2fDR06FACAgKMbZPJlKPOCy+8wOjRo63K4uPjSUtLw8HBgdjYWOrUqUOxYsW4evUqbm5uxMTE0K9fP+rWrUtoaCjffPMNBw8eNNpZuHAhjRo14q233iItLY1Ro0bh7u6eo0+LFi3i4MGD2NnZUbFiRaZNm0ZycjIDBw4kPDw813NKS0vj9u3bXLp0iczMTJydnSlVqhSJiYlUrFiRpKQkMjMzqVChAgMHDmTChAnUq1ePlJQUhg8fTps2bShRokS+XF8RERERkceRpkcWAm3btuXgwYMA7Nixg65duwKwfft2vLy8cHJy4sKFC3kef+jQISMxc3BwIDAwkMjIyBz1EhISKFasGE5OTuzZs4fvvvsOR0dH1qxZk2fb+/fvp3HjxjRo0IBDhw4B4OvrS0xMDAAxMTF07tyZS5cuUbp0aerVqweAo6Mjq1atUsImIiIiIvIHKWnLR8uXL8dkMmEymVi4cGGudTZu3GjU+cc//gFA165diY6OBrISsGeeeQaA6OhoOnToQJcuXXJNwvJSo0YNEhMTjT716tWLdu3aUbVqVVq2bEnjxo3p378/8+bNo3379mzatCnPtqKiovD29sbb25utW7cC0KVLF/bs2WP00dfXlytXrlCzZk0Ajh07hslkwt/fnyNHjjx0v0VEREREJCdNj8xHv3d6ZP369Tlz5gyXLl2iUqVKFCtWjOvXr3Py5EnGjh2L2Wzm9u3bDBs2LNe4ZrPZavvKlSvG2rrsPs2YMQNHR0cAYmNjadu2LX5+fiQkJDB48GCee+65HKNiZrOZAwcOGAng6dOnSU9Px8XFhYyMDG7cuMGdO3coX748KSkpRr3GjRsTGhrKokWLSE1NfZRLKCIiIiIi99BIWyHRuHFjli1bZjU1cuTIkYSEhLB69WoqV67MqVOncj3W09OTDRs2AFlr0FasWGG0k238+PGsX7+exMRE9u7dy8cffwxAhQoVcHZ2JjMzM0e7Bw4coFu3boSEhBASEoKvry9fffUVAB06dGD+/Pl4eXkBUL16da5du8bJkyeNfvz444/Y2dnlw9UREREREXl8aaQtHy1fvpyNGzca24mJicbIW61atejZsycbN2401q8BzJo1i+rVq9O1a1eGDBnCG2+8AWStbZs1a5ZRz8fHhy1btuDh4ZEj7tixY5kxYwaffPIJmZmZBAUF4e7uzvHjx406JUqUYOjQobz//vtMnTqV6dOn8+KLL+Lo6EhgYKAxCne37du34+/vb2z7+voSFhZGp06d8PHxYc6cOYwZM8bqXGbMmMHNmzcxm820bduWZs2a/Z5LKSIiIiIi/5+StnwSEBBgNTUyL9lr1+7VoEED9u/fb2yHhIRY7b87eWrZsiUtW7Y0tosXL87bb7+da5/ubSO7nZkzZz6wr/e22bx5c5o3bw6Ai4tLjvVqbm5uLFq06IHtioiIiIjIw1PSJkDWe+buHiUECA4Opk2bNgXUIxERERERASVt8v897EihiIiIiIjYlh5EIiIiIiIiUogpaRMRERERESnElLSJiIiIiIgUYkraRERERERECjElbSIiIiIiIoWYncVisRR0J0RERERERCR3euS/FJikpCSbxnNycgIg7fwFm8Z1qFmdOyd/smnMEvXcgYK5xuar12wa0961AgBpP8fZNK7DEzUwJ1y1aUx7N1cArq8Ks2lclwF9Cuz7erqtr03jPrk3ijsnTtk0Zon6HkDBfF8L4l4CSD1zzqZxi9epRVpcvE1jAjjUqGbzuA41qtn8XoKs+yn94mWbxixWtTLp8RdtGhOgWLWqBXIPA6SePmPbuE/WsWk8yZumR4qIiIiIiBRiStpEREREREQKMSVtIiIiIiIihZiSNhERERERkUJMSZuIiIiIiEghpqRNRERERESkEFPSJiIiIiIiUogpaRMRERERESnElLSJiIiIiIgUYkraRERERERECjH7h604fvx4/Pz86Ny5MwAjR45k9+7deHp6GnW6d+9Or169AAgKCsLHx4d+/foBYDKZjHq3b9/mxRdfJCgoiLNnzzJz5kwAypUrx/Tp0ylVqlSO+IsWLWLnzp04OTnx22+/MWPGDOrXr0/Xrl1xdXU16g0YMIDOnTtz8OBBFi5ciMVioUSJEkyZMoU6depgMpkIDQ1l0qRJ1KpVi2HDhgEwadIkRo0axeLFizl79izFixcHoGXLlvj5+fHGG2+wdu1aihQpwltvvYW3tzdbtmwx6qampjJixAjq1q3L4sWLmTNnjlXfn376aVq2bGkVq1q1aly4cIEePXqwa9cunJ2dWblyJdHR0Zw9e5aqVatSvHhxFi1ahLOzM5MnT8bZ2ZmJEydy8+ZNRo8eTWpqKhcvXqR27dp06tSJ5ORkI9b27dtZvXo1AHXq1OGNN97AwcGBRo0aERkZSfXq1Y3PJjQ0NM/P3tvbmylTptCxY0e+/PJLvvvuOyZMmABAWloaQUFBrFu3Ls9rLiIiIiIiv98jJW3Dhw/Hy8uLb7/9ljJlyuDp6Znrf/avXr2Km5sbMTExRtIGGHXNZjNBQUEEBQXx/vvvM3PmTCpVqsSmTZtYs2aNkUjda8qUKbRs2ZK4uDjmz5/Pv//9b1xdXXP04caNG8yfP58lS5bg7OxMXFwc//rXv1i0aJFVvXXr1uHv74+bm5tV+b///W+qVatmVdasWTM++eQT6tSpw507d2jbti1btmwx6iYnJzN8+HBmz579sJcUgG3btvHcc88RFRVFr169eOWVV3jllVesEjvISo5u377NpUuXyMzMxNnZmdDQUOLj462SxOxzPH/+PJ9++ikrV67EwcGBjRs3snjxYsaNG4ejoyPvvPNOjuuRm6NHj9KqVSu2bt1Kx44dadeuHR988IGx/8CBA7Rt2/ahr7mIiIiIiDyah54eWalSJTp16sSGDRtYtmwZr7/+ep51t2/fjpeXF05OTly4cCHH/uTkZNLT0wFwdnZm7dq1XLp0ieeee47Bgwc/sC8pKSmUKVMmz/27d++mW7duODs7A1CjRg0WLlyYo96gQYOYN2/eA+MBjBgxgvXr1/Pee+8xceLEHPt//fVXY3TuUezbt48xY8YQFRV133r79++ncePGNGjQgEOHDj2w3cjISAYMGICDgwMAL7zwAnv37gXA3d0dOzs7Dhw48MB2vvjiCwICAkhISCAlJQUHBwdq1qzJuXPnANixYwd+fn4Pfc1FREREROTRPNKatsGDB7Ny5Uq8vLyoUKECkDW1Lvvn8OHDAERHR9OhQwe6dOlCZGSkcbzJZKJbt2688sorRuIzfvx4zGYzJpOJ3r17ExcXl2f8WbNm0a9fP/r370+PHj2ArFG9u/tw4cIFrly5Qo0aNQBYv349JpMJPz+/HO116NCBGzducOTIEavycePGGe3t2LEDAEdHRzw9PXFxccHFxSVH3TfeeOO+iey97OzsuHjxIhUqVKBChQoULVqUhISEPOtHRUXh7e2Nt7c3W7dufWD7ly9fplKlSlZlJUqUMP48YcIE5s2bR0ZGxn3bOXLkCE2aNKFt27ZER0cD0LVrV6Kjo8nMzOTChQvUqVPnoa+5iIiIiIg8moeeHglQsmRJmjRpQpcuXYyye6cmXr9+nZMnTzJ27FjMZjO3b982pjuGhoZy/fp1Bg4cSJUqVQA4ceIEEydOZOLEiWzbto0PPviAuXPn5ho/e3rkrVu3GDRoEJ9++mmu0yNdXV1JTEwEoFevXvTq1ctqTd3dJk2axBtvvGEkHJD79MgzZ85w9uxZ7O3t+e6772jWrFmudePj43PEKF68uDGyCPDbb7/h4ODA5s2biY2NZdCgQSQkJLB161YGDhyY43iz2cyBAweMczp9+jTp6ekUK1Ys13MCcHFxISEhgdq1axtlmZmZxp+rVatG27ZtWbduXZ5tHD9+nMuXLzN48GDu3LmDk5MT3bt3p127doSHh/O3v/2NFi1aAI92zUVERERE5OHl+9Mjt2/fzsiRIwkJCWH16tVUrlyZU6dOGftdXFx47bXXmDVrFgBz5841plA++eSTDxz5AShVqtR9E5aOHTsSERHBrVu3AEhISODixYu51q1Tpw6NGjVi165debZnsVj45z//yaRJk5g6dSqzZ8/GbDY/sJ/Zateuzddffw1krU3LHmGLjo5mzZo1hISEEBoayrZt23I9/sCBA3Tr1o2QkBBCQkLw9fXlq6++um9MPz8/VqxYQVpaGgCffPIJzZs3t6ozbNgw1q9fn2cbUVFRTJ8+nZCQENauXcu1a9e4efMmxYsXp0yZMnz22WfGaNqjXHMREREREXl4jzTSlpu7R1NatGjB0aNHjYQMwMfHhy1btlgd4+XlxcqVKzly5AgzZ85k/PjxFC1aFCcnJ/75z3/mGWvWrFmUKVMGs9lMnz59gP9Nj8zm5+dHnz59eP311xk7diyZmZlkZGTkug4t2+jRo9m8ebOxPW7cOGN9Wq1atXjqqado0KABHh4eALRp0+a+T1uMiYkhICAAgB49ehAUFMTOnTt56aWXABg6dCiJiYkUL14cJycnIOvJmY6Ojpw9e9ZqdAyyEmF/f39j29fXl7CwMDp16pRnHzw8PPD398dkMmFnZ0fdunV58803reqULFmSIUOGEB4enmsbe/fuZfTo0cZ2x44d+eKLL+jduzc+Pj4sXbrUeDqki4vLI11zERERERF5OHYWi8VS0J2Qx1NSUpJN42UnyGnncz4c58/kULM6d07+ZNOYJeq5AwVzjc1Xr9k0pr1r1vratJ/zXg/7Z3B4ogbmhKs2jWnvlvV6k+urwmwa12VAnwL7vp5u62vTuE/ujeLOiVMPrpiPStTP+oVgQVzjgriXAFLPnLNp3OJ1apEWl3P5wp/NoUY1m8d1qFHN5vcSZN1P6Rcv2zRmsaqVSY+3/ayeYtWqFsg9DJB6+oxt4z6pVzcVFn94pO3P8Nprr3Ht2v/+41ekSBHjfWPy55g5cyYnT560KluwYIHVQ1dERERERMT2CmXS9t577xV0Fx47U6dOLeguiIiIiIhILvL9QSQiIiIiIiKSf5S0iYiIiIiIFGJK2kRERERERAoxJW0iIiIiIiKFmB75LyIiIiIiUogVyqdHyuMh7ezPNo3nUPsJABLfWWjTuBUnjOGXkLxfxv5nKD8o64Xzt7ftsGncMn7eXHlrtk1jVpo+GYAzfi/ZNG6dbZ+S+O4im8asOD7rZfe3Po+0adyyzz1bIO9fAgrkHVcF9X0tiPe0FcS9BHBl2hybxq00bZLN3/kHWe/9K4h/cwrqPW3nXgqyacxan37ExdffsGlMgKrzZhTIPQxw7vk+No1b6zPbvstR8qbpkSIiIiIiIoWYkjYREREREZFCTEmbiIiIiIhIIaakTUREREREpBBT0iYiIiIiIlKIKWkTEREREREpxJS0iYiIiIiIFGJK2kRERERERAoxJW0iIiIiIiKFmJI2ERERERGRQkxJWz6JiIggIiLC2J40aRJxcXH06NEDi8VilP/jH//ghx9+wGQyAWAymdiyZYuxP7vcYrGwaNEievbsSb9+/ZgxYwbp6elGvaCgINasWWN1XPbP888/z0cffZSj3GQysX79+jzbyMvkyZOZO3cuADdv3qR3795W+4cMGcK1a9e4cuUKwcHBBAYG0qdPH7788ssHXzgREREREbkv+4LuwP9lRYoUwdPTk6NHj9KkSRMyMzP56aefaNSokVW9//znP3Tu3JmSJUsaZZ9++ilms5nw8HAAli1bxo4dO3j22We5evUqbm5uxMTE0K9fP+OY0NBQAMxmM0FBQQQFBVmV3y2vNu6VlpbG7du3uXTpEpmZmTg7O1OqVCkSExOpWLEiSUlJZGZmUqFCBQYOHMiECROoV68eKSkpDB8+nDZt2lCiRInffxFFRERERB5zGmn7k/n6+hIdHQ3A4cOHadasWY46L7/8MsuXL7cq27JlC0OGDDG2hw4dyrPPPgvA9u3b8fLywsnJiQsXLuRoLzk52WpULjcPaiPb/v37ady4MQ0aNODQoUPGOcXExAAQExND586duXTpEqVLl6ZevXoAODo6smrVKiVsIiIiIiJ/kJK2P1nLli05fPgwADt37sTPzy9HncDAQPbs2cPFixeNsoyMDEqXLg3AqFGj6NOnjzFFMTo6mg4dOtClSxciIyONY0wmE926deOVV15h4sSJVuXZP9l9yauNe0VFReHt7Y23tzdbt24FoEuXLuzZs8dox9fXlytXrlCzZk0Ajh07hslkwt/fnyNHjjz6RRMREREREYOmR/7J7O3tqVmzJhcuXODUqVNWyVS2okWLMn78eN555x2jzMHBgdTUVIoXL87ixYuJj49n8eLFXL9+nZMnTzJ27FjMZjO3b99m2LBhQNY0yOvXrzNw4ECqVKlitHXv9Mj7tXE3s9nMgQMHSExMBOD06dOkp6fj4uJCRkYGN27c4M6dO5QvX56UlBSjXuPGjQkNDWXRokWkpqb+8YsoIiIiIvIYU9JmA76+vnz44Yc89dRTedZp3bo1a9eu5aeffgKga9eufPDBB4wdOxbImlppZ2fH9u3bGTlyJH369AFgxIgRnDp1ymjHxcWF1157jVmzZrF48eJcY+XVhoeHh1W9AwcO0K1bN/7+978DMHPmTL766is6depEhw4dmD9/Pl5eXgBUr16da9eucfLkSerVq0daWho//vgjLVu2/D2XTEREREQkT6fb+uZLO0/ujcqXdv5smh6ZjxYvXkxAQAABAQFWUx1bt27Njh07cp0aebeJEyeSkpICZK1zK168OH369MFkMhmJ1o4dO+jcubNxjI+Pj9XTJwG8vLxITk42pibePT1y4cKFD9UGZCV3nTp1MrZ9fX2Nej4+Pnz22Wf4+PgY+2fNmsX777+PyWSif//+NGrUKNc1fCIiIiIif4hdkfz5+YvQSFs+yU7WcmNvb8/+/futyrKnLN49dbF69er88MMPANjZ2REcHExwcLDVcSEhIVbb/v7+ucZctWpVjvbzklcbb7/9ttV28+bNad68OZA1onfvejU3NzcWLVr0wHgiIiIiIvLwlLQJkPWeuY0bN1qVBQcH06ZNmwLqkYiIiIhIHuzsCroHNqWkTYD7jxSKiIiIiBQmdkUer6TtrzORU0RERERE5DGkkTYREREREflr+Qs9RCQ/KGkTEREREZG/lsdsTdvjlaKKiIiIiIj8xdhZLBZLQXdCRERERETkYcV2eT5f2qm787N8aefPpumRIiIiIiLyl2L3mE2PVNImBea3Iz/YNF7JJo0ASHxnoU3jVpwwhuuh620a08XUC4Ck7dE2jevk04krb822acxK0ycDcLqdn03jPvnVNhLnLbZpzIqvjwLg9pYom8Yt092X9EtXbBqzWJVKAKT9HGfTuA5P1Ci472tSkk3jOjk5Fci9BJAw812bxnWbOp6z3XraNCZA7a3hJL67yKYxK44fbfN7CbLup/N9Bts0Zs2wD7k0aZpNYwJUmTONhFn/tmlMtynjADjja9vXM9WJirBpvEdS5PFa5fV4na2IiIiIiMhfjEbaRERERETkr0XTI0VERERERAqxxyxp0/RIERERERGRQkwjbSIiIiIi8pdi95g9iERJm4iIiIiI/LUoaRMRERERESnEtKZNRERERERECguNtImIiIiIyF+K3WM20qak7Xfy9vZmypQpdOzYka5du+Lq6gpARkYG77zzDtWqVctRD7Cqe+vWLUaOHEmZMmVYsmQJV69eBcDV1ZURI0YwdepU+vbty6BBgwCIjIzk3XffJTo62qodgAEDBnDr1i22bt1KSEgIABERWW+x//rrr7l8+TInTpygfv36VK5cmVmzZjFjxgzi4uKwWCyMGTMGT0/PPM938uTJODs7M3HiRG7evElwcDDr1q0z9g8ZMoTZs2djNpuZNm0at27dws7OjkGDBtG5c+d8ueYiIiIiIgAUUdImD3D06FFatWrF1q1b6dixI66uroSGhgKwb98+wsLCmDBhQo56gFXd5ORkxo4dS0hICK1btzaSrICAAAAqVKjAwYMHjaRt//79lCtXLkc72SIiIjh+/DhffvmlVaL0zjvvAGAymaz6WbFiRaZNm0ZycjIDBw4kPDw81/NNS0vj9u3bXLp0iczMTJydnSlVqhSJiYlUrFiRpKQkMjMzqVChAgMHDmTChAnUq1ePlJQUhg8fTps2bShRosQfv/AiIiIiIo8hrWn7Hb744gsCAgJISEggJSXFal9KSgqOjo4PrAdw8+ZNMjMz84xTvHhxihcvTnJyMpmZmaSlpRlt52XgwIEsXryYtLS0+9ZzcnJiz549fPfddzg6OrJmzZo86+7fv5/GjRvToEEDDh06BICvry8xMTEAxMTE0LlzZy5dukTp0qWpV68eAI6OjqxatUoJm4iIiIjkL7si+fPzF/HX6WkhcuTIEZo0aULbtm2Jjo7m6tWrmEwmgoKC+OKLL+jXr1+u9QCjrq+vLxMmTGDKlCn3jdWmTRv27dvH4cOHadKkiVGe3U72z4ULF4CsETg/Pz9WrVp133YbN25M//79mTdvHu3bt2fTpk151o2KisLb2xtvb2+2bt0KQJcuXdizZw8A0dHR+Pr6cuXKFWrWrAnAsWPHMJlM+Pv7c+TIkfv2RURERETkkRSxy5+fvwhNj3xEx48f5/LlywwePJg7d+7g5OSU61TF3Op1797dqHv+/HnGjBlD5cqV7xuvQ4cOLFiwgPLly9OnTx+2bdsG5D49MnsUbMCAAfTq1Qs/Pz8qVKiQa7uxsbG0bdsWPz8/EhISGDx4MM8991yOUTGz2cyBAwdITEwE4PTp06Snp+Pi4kJGRgY3btzgzp07lC9fnpSUFKNe48aNCQ0NZdGiRaSmpj7k1RURERERkXtppO0RRUVFMX36dEJCQli7di3Xrl3j5s2bj1yvZs2a9OzZk0WLFt03npubG7/88gsJCQkPTPCyOTg4MHLkSOOBJLnZu3cvH3/8MZC1ds7Z2TnXqZoHDhygW7duhISEEBISgq+vL1999RWQlVDOn///2Lv3+Jqu/P/jr1xJSBAiCVU0jEunP1Jp1VBBtIJS4lbqpHWpMSJVLZOWthqt9D5tJbSliUtQWmKYFEGJW5HRcRmKkpaKSyQISSP3/P7wdUYkgvbknJye9/PxOI/mrL32+qy1c5L0Y6299scEBAQA0KhRIzIyMjhy5Ahw7V64Q4cO2dzuPiIiIiJSuezs7EzyshZK2u7S9u3b6dChg/F9165duXTp0h3VW7duXak6Q4YMYefOnZw5c6bCmK1bt6Zhw4alym5eHrlkyZJSx7t37879999/yzaHDRvGyZMnGTBgAM8++yxDhw4t93659evX061bN+P7Hj16kJCQAMDjjz/OqlWrePzxx43HIyMjmTVrFgaDgWeeeYYHHniAdu3aVTg+EREREZG7YmP3tGl55F1auXJlqfehoaGEhpBpNTAAACAASURBVIbeUT2Ap556yljm6OjI6tWrje+v7xp53fXljxMmTChTdnMCWJ7Y2Nhy24Nrs3EzZsy4bRtvvvlmqff+/v74+/sD4OHhUeZ+NS8vr9vOHoqIiIiI/C5WdD+aKShpE6P4+PgyyebYsWPp2LGjhXokIiIiIiJK2sQoODi4zGyfiIiIiEhVY2dvPUsbTUFJm4iIiIiIWBcr2kTEFGwrRRUREREREbkDFy5cYNy4cfj7+9O+fXtmzJhBYWFhuXUXLFhAt27dePDBB+nTpw+JiYkm7YuSNhERERERsS52dqZ5VeCFF17A1dWVbdu2sXz5cnbu3Mn8+fPL1NuyZQuff/45X3zxBf/5z38YP348L7zwAqmpqSYbrpI2ERERERGxLvb2pnndwsmTJ0lOTmby5Mm4uLjQqFEjxo0bx+LFi8vU/emnnygpKTG+HBwccHJywtHRdHei6Z42ERERERGxKpX9YOxjx45Ru3ZtvLy8jGW+vr6cOXOGK1eu4O7ubizv3bs38fHx9OrVCwcHB+zs7Hj//ffx9vY2WX/sSkpKSkzWmoiIiIiISCX75Zm/maSdexd8Wm75qlWr+Oijj0hKSvpfzF9+4bHHHmPLli2lErLTp0/z8ccfYzAYaNmyJf/617946623WLp0KS1atDBJPzXTJhZTmJ5h1niOnvUAyPp2i1njugUGcOzRnmaN2XzbWgByDx0xa9zq97ckKyvLrDHd3NwAuBi3zKxxPQxDuPrfQ2aN6fLA/QBcXr3GrHFr9e1FwZlzZo3p1ODaH8Pcw0fNGrd6qxYc69TDrDGbb792s7olfnYs8VkCSB0/2axx74l+nwsxcWaNCVB3lIHspO1mjVmzSyezf5bg2ucpY3aMWWPWGzeKnH//x6wxAVwfepDU58PNGvOeme8CcHH+ErPG9Xh2mFnj3ZVKfri2q6srV69eLVV2/X2NGjVKlb/55ps8+OCD/L//9/8AGDBgAAkJCaxcuZKXX37ZJP3RPW0iIiIiImJd7OxN87qF5s2bk5mZSUbG/yYZUlJS8Pb2Nv6D8XVnzpwhPz+/VJmjoyNOTk4mG66SNhERERERkRs0adKEdu3aERkZSXZ2NqdOnWL27NkMHDiwTN1u3bqxaNEiDh06RHFxMevWrWP37t306tXLZP3R8kgREREREbEuZni49syZM5k+fTqBgYHY29vTr18/xo0bB4Cfnx8RERH07duX8ePH4+DgQFhYGJcvX6Zx48bMmjWLVq1amawvStpERERERMSq2FXyPW0A9erVY+bMmeUe27t3r/FrR0dHwsLCCAsLq7S+aHmkiIiIiIhIFaaZNhERERERsS5mWB5ZlShpExERERER62JvWwsGlbSJiIiIiIhVsbOxpM22RisiIiIiImJllLSZUHx8PEFBQRgMBgwGA1988QXx8fGl6uTn5xMREYHBYGDo0KGsXbvWeGz9+vU8/fTTPP3007z++uvGh/T5+/sb2+zbty/ff/89ABs3buSZZ55h5MiRREVFGfvg7+9PYWGhsd2ePXsSHx/P7t27iYqKIjU1lQ4dOpCVlQVAampqhU9rz8nJ4cEHH+THH38EYOHChSxevNh4PC0tjbFjxwKQmJjIU089xfDhwwkNDSUtLe03X08RERERkXLZ2ZnmZSWUtJnYmDFjiIuLIy4uDg8PjzLHP/vsMx544AHi4uJYsGABK1euJCUlhZMnT7J8+XLmzZvH4sWL8fPzIzo6GoBWrVoZ25w9ezZLliwBYMGCBcybN4/Y2FjOnj1r3Hq0du3a/Oc//wHg1KlT5OTklOlHcXGxsf3bSUpK4oknniAhIQGAHj16kJSUZDy+ceNGevToQUpKCl9//TXz589n0aJFjBw5kk8//fTOL56IiIiIyJ1Q0iaV6d///jfBwcEAODs7M3ToUNasWcOaNWt49tlncXZ2BqB///5s3769zPlpaWk4OTkB12bAVq9eTVZWFhEREbRt2xaALl26sGXLFgA2bdpE165dy7TTpUsX9u/fT0pKym37vH79ev72t7+xe/duALy8vMjLyyM7OxuArVu3EhgYyNq1axk+fDjVq1cHoF27dkybNu2uro+IiIiIiJSmpM3E5syZY1zKWFJSctv69957L+fPn+fs2bN4e3uXOnY9+Tl8+DAGg4Fu3boxZ84cJk6cCMDHH3/Mjh076NGjB3//+9/Jzc0FoHXr1hw5cgSAgwcP8sADD5SJa2dnx8svv8y7775bYf9yc3PJzMzEx8eHFi1aGGfzunXrxvbt27l8+TJOTk64u7uTlpbGvffeC0B0dDQGg4GQkJDbXgMRERERkbtib2+al5Wwnp5aiRuXR9qVM+V6471mAOfOncPT0xMPD48y938VFxcD/1se+eGHH3L58mXq1q1LdnY2ubm5vP/++2zdupW6deuyZs0a47kNGzbkxx9/pEaNGuX2A6Bt27bUqlXLOCtXni1btnD27FlGjRrFoUOHjEskg4KCSEpKYvPmzXTv3h0AT09Pzp8/D8D48eOJi4u73eUSEREREblrdnZ2JnlZCyVtZubn58eKFSuAa5uSxMbGEhQURM+ePYmNjTVuPvL111/j7+9f5twHH3yQJUuWkJ+fzyuvvMLVq1dxdHSkadOmxiQPICAggHfffZdOnTpV2J9JkybxxRdf3PJ4YmIis2fPJiYmhq+++opdu3ZRVFSEt7c3mZmZbNu2jcDAQAB69epFbGwseXl5AKSkpHD58uW7v0giIiIiIhXRPW1iStHR0QQHBxMcHExycjITJkxg3759xh0W+/fvz5/+9CdatGhBv379MBgMPPXUU+zfv5+wsLAy7Y0dO5Yvv/wSZ2dnRo8ezdChQzEYDBw5coR+/foZ6/3lL3/hwIEDdOzYscL+eXl5MWTIkHKP5efnc+LECXx9fQFwcHCgTZs2fPfddwC0b9+evLw83NzcAGjWrBlDhgxh9OjRhISEEBERwdSpU3/TdRMRERERkWv0cG0Tur7ByI3vby4DePPNN8s9v3fv3vTu3btM+Y3LDN3d3Y2PCQgKCiIoKOiWffj3v/9dpqx9+/YAvPPOO8ay69v138zZ2bnMIwsiIyONX48YMYIRI0aUOh4YGGiceRMRERERqRT21jNLZgpK2sRo4sSJZGRkGN/b29uzYMECC/ZIRERERKQcdra1YFBJmxh99NFHlu6CiIiIiIjcREmbiIiIiIhYFTstjxQREREREanCrOgZa6ZgW6MVERERERGxMpppExERERER62JFz1gzBSVtIiIiIiJiVexsLGmzKykpKbF0J0RERERERO7U2anlP/f4bvnMeM0k7VQ2zbSJxRScOWfWeE4NvAG4kpBo1rjuT/SgMD3j9hVNyNGzHgC5h46YNW71+1tyrFMPs8Zsvv3a9/PsazPMGtfnzamc/yDKrDHrTwoDIP/kKbPGdW7ciKysLLPGdHNzA7DIz46lfl4tcY0t8VkCyP8l1bxx773H7L8j4Nrvicv/WmfWmLX6BJn9swTXPk+Xlnxt1ph1hg3i7KtvmTUmgM9br1rkMwxw/t2PzRq3fvgLZo0nt6akTURERERErIuNLY9U0iYiIiIiItbFxpI2bfkvIiIiIiJShWmmTURERERErIqdjT1cW0mbiIiIiIhYFxtbHqmkTURERERErIu9bSVttjWvKCIiIiIiYmU00yYiIiIiItZFyyNFRERERESqLlvbiMS2RisiIiIiImJlNNNmAvHx8cyZMwdPT08AatWqxQ8//MAHH3zAgw8+CIDBYGDcuHHMnj2b9PR0ADw9PRk3bhwRERHGcy9fvkxoaCg9evQA4Ouvv2b58uUsW7bMGOubb74hJibG+B4gODiYFStWEB8fT2FhIe3bt+fFF18kKiqKjRs34u7uDoCvry9vvPEGGzduJC4uDgcHB/z8/AgLC7vl+Hbu3El4eDibN2/GwcGB8ePH88ILL9CsWTMAEhISSEtLY+TIkURHR7Nt2zacnZ1p2bIl4eHhODk5mfR6i4iIiIiNs7OtuSclbSYyZswYgoODje/79OnDe++9x5dffond/6257dChAx06dCiVaMG15C0uLg6A7OxsJkyYYEzakpKSaNKkCT///DNNmzYF4ODBg3z77bcEBgYa4x06dIgdO3YQFxeHvb09r7zyCklJSQBMmTKF9u3bl+rvggULWLBgAfb29kyZMoW9e/fi5+dX7tjWrVtHly5d2LlzJ506dSIoKIhNmzYZk7aNGzcyadIkli9fTmFhIV999RUAn3/+ORs2bKBXr16//cKKiIiIiNxMu0eKKdSuXRs/Pz9WrFhxV+dlZmZSXFwMwJUrVygqKqJXr14kJCQY61yf0crPzzeWrV+/nkGDBmH/f+t7X3vtNTp16nTLODk5OaxevZqsrCwiIiJo27ZtufWKi4tJSUkhJCTE2IcuXbqwY8cOAPLz88nMzOSee+4hISGB5557znjumDFjlLCJiIiIiPxOmmkzkTlz5rBy5UoAHnroIQBCQ0MZNmwYQUFBFZ6bnp6OwWDg/Pnz1K1bl4iICAC+/fZbunTpQocOHZg5c6ZxCaOnpyc9e/Zk/vz51KtXD4CLFy9Sv359Y5uurq7GryMjI43LI3v27MmwYcP4+OOPmTlzJu+99x7t27cnMjISFxeXMn3bs2cPfn5+NGvWjOPHj5OXl0fNmjVxc3PjwoULHDhwgM6dOwNQVFREzZo1ARg/fjwXLlygbdu2hIeH3/0FFRERERG5BTvtHim/xc3LIw0GAzVr1sRgMDBr1qwKz72+PPLkyZM8//zz+Pj4ANdmz7Kzs9mwYQNpaWkcPHjQeM6zzz7LkCFD6NmzJ/Xq1cPT05OzZ8/i6+sLwOnTp8nKygLKLo/Mzs4mNzeX999/n8LCQt555x3WrFnDgAEDyvQtMTGRgwcPMmrUKDIzM0lKSqJHjx489thjbN68mb179zJu3DgAnJ2dycvLo1q1akRHR5Oamkp0dPRvvKIiIiIiIrdgY/e02dZoLWDAgAHs3buXzMzM29Zt3LgxgwcPJioqiuzsbLKysoiLiyMmJoY33nij1BJJZ2dnQkNDjRuSdO/ena+//pri4mJKSkqIiori9OnT5cbJz8/nlVde4erVqzg6OtK0aVPjkswblZSUsHfvXpYuXUpMTAxRUVHGPgQGBrJz504uXrxIw4YNAQgKCuKzzz4znr93716b+1cQERERETEDezvTvKyEZtpM5MblkQC5ubkA2NvbM3nyZCZPnnxH7QwZMoTg4GBatWpFx44djeWdOnXi/fffp1mzZsb71rp3786SJUsAaN26NQ8//DBDhw6luLiYv/zlLwQGBvLDDz+UWh5Zs2ZNPv30U0aPHs3QoUNxc3OjSZMmvP7662X6snfvXlq3bm1MvFq1asXPP/9MVlYWbm5u5OXl8fDDDxvrDxo0iM8//5xhw4bh4OBA7dq1tTRSREREROR3UtJmAsHBwaWWRt6sXbt2bNq0qVT9G13fORLA0dGR1atXl2mjevXqJCYmlimPjY01fv3000/z9NNPlzoeFhZW7nb+QUFBt73X7sEHHzQ+suC6G2f7bl76aGdnx9ixYxk7dmyF7YqIiIiI/C42tppLSZsA1zZDefHFF0uV3X///bz88ssW6pGIiIiISPnsrGhpoykoaROg9LPiRERERESk6lDSJiIiIiIi1sXGdo9U0iYiIiIiItZF97SJiIiIiIhUYTZ2T5ttzSuKiIiIiIhYGbuSkpISS3dCRERERETkTmXMmmuSduqFPmeSdiqblkeKiIiIiIh10UYkIuaRlZVl1nhubm4AFKSeNmtcp3saUnAuzbwxvb0AKEzPMGtcR896lvu+WuAaW2qsloh77NGeZo3ZfNtawDJjLThzzqwxnRp4A5YZq039Hj591qwxAZwa+pg9rlNDH7N/huHa59gSYzX3Zxj+7/eEBT7DgMV+P4nlKWkTERERERHrYmMbkShpExERERERq2KnLf9FRERERESqMBtL2mzrDj4REREREREro5k2ERERERGxLva2NfekpE1ERERERKyLlkeKiIiIiIhIVaGZNhERERERsSraPVJERERERKQq0z1tUpU89thjTJkyha5du9KtWzeefvppRo0aBcCaNWv44IMPmDFjBrNnzyY9PR0AT09Pxo0bR4cOHcq0l5SURExMDMXFxTRu3Jjp06ezevVq5syZg6enJ4WFhbi6uvLpp5+SkJBgLAeoVasW0dHRBAUF4enpSVFREe7u7kRGRuLh4YHBYCAqKoqwsDDy8vI4ffo09913H926dWPEiBHmu2giIiIiIn8gStqqsP379/PII4/wzTff0LVrV+rVq0dycrIxafvuu++oU6cOHTp0oEOHDsTHxwMQHBxcbntpaWksWLCAmJgYnJ2diY6OZtmyZbi4uDBmzBjjeVFRUSQnJwOUKr/O09OTuLg4APbs2UNUVBTTpk0DoHbt2sTFxZGamkp0dDTvvPOO6S+MiIiIiNg2G1seaVvzilZm3bp1BAcHk5aWRk5ODtWqVaNatWpkZ2dTXFxMfn4+rq6ud9ze1q1b6dWrF87OzgA899xzDBw4sEy9tLQ0nJyc7qhNf39/UlNT77gPIiIiIiK/m729aV5Wwnp6aoP27dtH27Zt6dSpE5s2bQKgY8eO7Nixg71799K2bdu7au/ChQvUr1/f+P56EggwZ84chgwZwqOPPkrDhg1p3769sdxgMGAwGJg5c2a57ebl5f2W4YmIiIiI/CZ29nYmeVkLLY+sog4ePMjZs2cZPXo0ubm5uLm5AdClSxc++eQT6taty7Bhw1i7du0dt+np6cm5c+eM7zMzMzl+/Djwv2WQb731VqnZu/KWR96suLj4boYmIiIiIlLlXbhwgddee43k5GQcHBzo27cv4eHhODreOoX68ccfGTRoEHPmzDFOgpiCZtqqqMTERCIiIoiJiWHx4sVkZGSQmZmJl5cXFy5cIC0tDR8fn7tq89FHH2XNmjXk5+cDsHDhQn744YdSdSZNmsSyZcs4f/78HbW5a9cufH1976ofIiIiIiK/i52daV4VeOGFF3B1dWXbtm0sX76cnTt3Mn/+/FvWv3r1Ki+99BK5ubkmHqxm2qqs7du3ExYWZnzftWtXvvzySwBat279m9qsX78+Q4cOJSQkBIBmzZoRGhrKqlWrjHWqV6/OmDFjmDVrFm3atGHOnDmsXLnSeHzu3Lmkp6djMBgoLi7G3d2dt99++zf1R0RERETkN7Gr3LmnkydPkpyczNatW3FxcaFRo0aMGzeO999/n9GjR5d7TkREBN27d+fHH380eX/sSkpKSkzeqsgdyMrKMmu860tMC1JPmzWu0z0NKTiXZt6Y3l4AFKZnmDWuo2c9y31fLXCNLTVWS8Q99mhPs8Zsvu3a0m9LjLXgzLnbVzQhpwbegGXGalO/h0+fNWtMAKeGPmaP69TQx+yfYbj2ObbEWM39GYb/+z1hgc8wYLHfT1XRpS9XmKSdOkMHlFu+ceNGpk6dyu7du41lR48epW/fvvz73//G3d29VP1//vOfLFu2jEWLFtG6dWsWLlxo0uWRmmn7g5ozZw7btm0rVfbqq6/SokULC/VIRERERMQ0KnsTkV9//RUXF5dSZdff5+TklEraUlJS+Oijj/jyyy9xcHColP4oafuDGjNmDGPGjLF0N0RERERETK+Sn9Pm6urK1atXS5Vdf1+jRg1jWV5eHhMnTmTKlCk0aNCg0vqjjUhERERERMS62Nmb5nULzZs3JzMzk4yM/91qkpKSgre3t3GpN8B///tfTpw4wdSpU/H398ff3x+AsWPH8sYbb5hsuJppExERERERuUGTJk1o164dkZGRTJ8+nUuXLjF79mwGDhxYqp6/vz8HDhwoVdaiRQs+++wzbfkvIiIiIiI2zN7ONK8KzJw5k8LCQgIDAxk8eDCPPvoo48aNA8DPz4/Vq1ebY6SAZtpERERERMTK2FXyPW0A9erVY+bMmeUe27t37y3PO3r0qMn7oi3/RURERETEqlyO/5dJ2qkV3Mck7VQ2zbSJiIiIiIh1qeQt/6saJW1iMZZ48DNAdtJ2s8at2aUTuQcPmzVm9T+3AiD3sOmn5yuM26oF5z+MNmvM+i+NByD9o1lmjes5MZRjnXuZNWbzrWsAyD3yo1njVm/5J4s9hNkSvycs9fNqiWtsid8RADnJ35s1ruvD7cz+uwmu/X7K3rTVrDFrdutssQdOX1rytVlj1hk2iIzZMWaNCVBv3CiLfIYBLi3+yqxx6zw92Kzx7oq9bW3NYVujFRERERERsTKaaRMREREREetSwTPW/oiUtImIiIiIiFUxx+6RVYmSNhERERERsS42thGJbc0rioiIiIiIWBnNtImIiIiIiHXR8kgREREREZEqzMY2IrGt0YqIiIiIiFgZzbSJiIiIiIhVsbOxjUiUtP1O8fHxAAQHBxvLdu7cSXh4OJs3b8bBwYHdu3czffp0Vq1ahaOjI7t37yY5OZmHH36YV155hYYNG5Kbm8tjjz3GmDFjjO2EhITw+OOPM3z4cAAMBoPx2JUrVxgwYAAhISGlygGeeOIJhgwZUm4bN0tNTSUwMJDly5fzwAMPADBt2jTy8vJ45513OH78OG+//Tb5+fm4uLgQERGBj4+PMWZJSQmOjo5ERETQuHHjCvsiIiIiImISuqdNfq9169bRpUsXdu7cSadOnQDIyMhgyZIlhISElKrbv39/wsLCAJg6dSr79++nTZs2pKen4+XlxebNm0slXHFxcQAUFhYSEhJibO96+Y1u1cbNGjVqxJYtW4xJ29GjR2nSpAmFhYW88cYbfPjhh3h5eXH8+HGmTp1KbGxsqZgnTpzggw8+ICoq6pZ9ERERERExGXvbusvLtkZrBsXFxaSkpBASEkJCQoKxfODAgaxYsYKLFy/e8tyBAweyfft2ANavX09AQABubm6cOnWqTN3s7GwKCgoq7Mvt2rjOz8+Pffv2AXDkyBFatmwJwP79+2nXrh1eXl4ANGvWjJo1a5Kamlrq/CZNmlBQUEBRUVGF/RERERERkbunpM3E9uzZg5+fH82aNeP48ePk5eUBUL16dcaMGcMnn3xyy3Nr167N+fPnAdi0aRNdunShe/furFmzxljHYDDQu3dvRowYQXh4eKny66+9e/dW2MbNHBwcqFOnDhcvXmTz5s107doVgHPnzuHj41OqbqNGjYx9vFGNGjW4dOnSLfsiIiIiImIqdnZ2JnlZCy2PNLHExEQOHjzIqFGjyMzMJCkpidq1awPQu3dvli5dytGjR8s9Nz09nbp163Lx4kWOHDnChAkTKCws5MqVK/z1r38Fri09vHjxIiNHjqRBgwbGc29eklhRG+Xp3LkzW7duJSUlhT59+gDg4eHB8ePHS9U7d+4cnp6eZc6/cOGCcZxaHikiIiIilUrLI+W3KikpYe/evSxdupSYmBiioqJKLZEEeOWVV/j000/LPT8+Pp6AgADWr19PaGgoMTExLFiwAB8fn1KJnoeHBxMnTiQyMvKWfbldGzfr3LkzCQkJ1KtXz1jWrl07duzYQVpaGnDtXrfz58/TqFGjUuf+/PPPuLi44OiofwMQERERETE1/V+2CURHR7No0SJ++OEHHnroIeNUa6tWrfj555/Jysoy1m3dujXdu3c3vl+5ciXJyckUFBTQrVs32rRpw8yZM0slZI8//niZ5C8gIIB58+YZ70W7cdfGhx56iP3795fbRosWLcodQ61atbh69SoBAQHGMmdnZ6ZPn87kyZPJz8+nZs2avP3228bjBoOBkpISnJ2diYiIKFV+Y1+ef/7521xBEREREZG7YEVLG01BSdvvFBwcXGq7/5tdT7ZuTNTefPNN49ebNm0qc05MTEyp9/369Su37fnz5wN3thzxVm3cc889vPPOOwAsXrzYWH69rGXLlixcuLDMebeKqaWRIiIiIlLp9Jw2+aOKj49n5cqVpcrGjh1Lx44dLdQjERERERG5HSVtNuR2s4IiIiIiItbAzs62tuZQ0iYiIiIiItZF97SJiIiIiIhUYTZ2T5ttzSuKiIiIiIhYGc20iYiIiIiIddE9bSIiIiIiIlWXnY0tj7QrKSkpsXQnRERERERE7lRO8vcmacf14XYmaaeyaaZNLCYrK8us8dzc3CwWV2Ot3JigsVZ23PNZOWaNWd/NFdD3tbLjaqx/vLhubm50eSParDEBkt4Yb5GxFqZnmDUmgKNnPZv62amytHukiIiIiIhIFaakTUREREREpOqys7etjUhsa7QiIiIiIiJWRjNtIiIiIiJiXWxspk1Jm4iIiIiIWBcbu6fNtlJUERERERERK6OZNhERERERsS429nBtJW0iIiIiImJV7Oxsa8GgbY1WRERERETEymimzQTi4+OZM2cOnp6e5ObmMnToUIKDgwF47LHHmDJlCl27diUlJYUpU6awdOlS7OzsOHToEB9//DFz587l448/5sCBAxQXFzN8+HC6d+9ebix/f39atWplfD958mS2bNlCYmIiCQkJABQUFPDII48we/ZskpOT2bhxI+7u7uTl5TFo0CAGDRqEwWAgLi6uVNvr169nwYIFAPj6+vLqq6/y/fffs3jxYqKjowHYsGED27dvp02bNsYxA9SqVYvo6GiCgoLw9PSkqKgId3d3IiMj8fDwMO0FFxERERHbZmMbkShpM5ExY8YQHBxMUVER48eP55FHHiE9PZ1HHnmEb775hq5du+Lr68sDDzzAqlWr6NevHx9++CGvvfYaJ06cID09ndjYWAoLCxk8eDABAQE4OTmVidOqVasyydaWLVvIzc3l9OnTNGzYkD179lCzZk3j8SlTptC+fXsKCgoYOnQogwYNKtPuyZMnWb58OfPmzcPZ2ZmVK1cSHR3Niy++yMKFC9mzZw9t2rRh7ty5fP7552zel1yRZwAAIABJREFUvNk45ht5enoa+7dnzx6ioqKYNm2aKS6xiIiIiMg1NnZPm5ZHmpiDgwO9e/cmOTmZdevWERwcTFpaGjk5OQCMHz+ehQsXsmHDBlq3bk3Tpk1xcXFh//797Nixg5KSEpYuXVpuwlaRLl26sGXLFuBaEhcQEFCmTl5e3i3bXbNmDc8++yzOzs4A9O/fn+3btwMQHh7OJ598wrJly+jfvz916tS5oz75+/uTmpp6V+MQEREREbktOzvTvKyEkrZKULt2bc6fP8++ffto27YtnTp1YtOmTcZj/fr144MPPuBvf/sbAF5eXkydOpXY2Fg6d+7M3Llzb9n24cOHMRgMxldubi4AHTt2ZNeuXQBkZGQYly0CREZGYjAYCAsL4/nnny+33bNnz+Lt7V2qrHr16gA0adKE1q1bs3btWoYMGWI8PmfOHGM/Zs6cWW67eXl5FV4rERERERGpmJZHVoL09HQcHR05e/Yso0ePJjc3Fzc3N5544gkAunXrxg8//ECNGjUAOHXqFL6+vsTExHDlyhXGjBlD3759adSoUZm2y1seCeDq6kpRURGHDx/mvvvuo6ioyHjs+vLIinh4eJCWlsZ9991nLCsuLjZ+3a1bN2rWrIn9DU+fL2955M1ubENERERExBS0e6T8LkVFRSQkJHDs2DEiIiKIiYlh8eLFZGRkkJmZWe45R48eNW704e7ujre3929Kdtq3b88HH3xAt27d7vrcnj17EhsbS35+PgBff/01/v7+d93OjXbt2oWvr+/vakNEREREpAx7O9O8rIRm2kxkzpw5rFy5koKCAoYMGcLChQuJiIgwHu/atSvr1q3jqaeeKnNuYGAgBw4coH///tSsWZPu3bvTuHHjcuNcXx55XUhISKkYsbGxtGzZkg0bNlTY34MHDxpnydq3b094eDj9+vXDYDBgZ2dHs2bNeO211+5ozNfNnTuX9PR0DAYDxcXFuLu78/bbb1fYhoiIiIiIVMyupKSkxNKdENuUlZVl1nhubm4Wi6uxVm5M0FgrO+75rByzxqzv5gro+1rZcTXWP15cNzc3urwRbdaYAElvjLfIWAvTM8waE8DRs55N/exUVXkpP5uknWq+TU3STmXTTFsVtXXr1jIbkgwYMIB+/fpZqEciIiIiIlWDnRXt/GgKStqqqM6dO9O5c2dLd0NEREREpOqxt62tOWxrtCIiIiIiIlZGM20iIiIiImJdtDxSRERERESkCrOxpE3LI0VERERERKowzbSJiIiIiIhVsbOiB2Obgp7TJiIiIiIiVqXg9FmTtOPU0Mck7VQ2zbSJxRScSzNrPCdvLwB+3bXHrHFrPOLP5dVrzBqzVt9eAOQdSzFr3GrNfcn/JdWsMZ3vvQeAKwmJZo3r/kQPcg8dMWvM6ve3BOBYpx5mjdt8e6LFHuh6MXaRWeN6jBxusZ9XS1zjY4/2NGvM5tvWApD+yWdmjes5YazZv69w7Xubk/y9WWO6PtzOYg8Sv/yvdWaNWatPkNn/psO1v+vpUZ+bNaZn2F8By/ytk6pBSZuIiIiIiFgXG9uIREmbiIiIiIhYFxu7p01Jm4iIiIiIWBU7O9vaBN+2RisiIiIiImJlNNMmIiIiIiLWRcsjRUREREREqq6r1auZpB03k7RS+bQ8UkREREREpApT0iYiIiIiIlKFKWkTERERERG5yYULFxg3bhz+/v60b9+eGTNmUFhYWG7dLVu20KdPH9q2bUvPnj3ZvHmzSfuipE1EREREROQmL7zwAq6urmzbto3ly5ezc+dO5s+fX6beiRMnCAsLY8KECezZs4ewsDBeeOEF0tLSTNYXJW13aNKkSXz77bfG96Ghofz5z3/GYDAYX8uWLTMeDwkJYdGiRcb3N9Z78sknWbhwIQA//fQTo0aNYtSoUUyaNIlff/213PhRUVE8+eSTDB8+nAEDBnD48GFjuze6/v7m/16XmppKx44dMRgMPPXUU7z55psVjjsnJ4cHH3yQH3/8EYCFCxeyePFi4/G0tDTGjh0LQGJiIk899RTDhw8nNDTUpB9UERERERFzOXnyJMnJyUyePBkXFxcaNWrEuHHjSv1/8HUrV67E39+f7t274+joSK9evXjooYdK5Qa/l5K2OzRp0iSio6MpLCxk165duLu74+fnR1xcnPE1ZMgQANLT0/Hy8iozLXq93ooVK1i3bh0As2bNYsaMGcTExNCpU6dSid7NpkyZwqJFi/joo4+YO3fubx7Lo48+SlxcHEuXLsXDw4M1a9bcsm5SUhJPPPEECQkJAPTo0YOkpCTj8Y0bN9KjRw9SUlL4+uuvmT9/PosWLWLkyJF8+umnv7mPIiIiIiKWcuzYMWrXro2Xl5exzNfXlzNnznDlypVSdY8fP86f/vSnUmXNmjXjyJEjJuuPkrY75O3tTbdu3VixYgWff/45L7300i3rrl+/noCAANzc3Dh16lSZ49nZ2RQUFABQu3ZtFi9ezJkzZ+jbty+jR4++bV9ycnJwd3f/7YO5waBBg9i+ffstj69fv56//e1v7N69GwAvLy/y8vLIzs4GYOvWrQQGBrJ27VqGDx9O9erVAWjXrh3Tpk0zSR9FRERERMzp119/xcXFpVTZ9fc5OTm3rVu9evUy9X4PJW13YfTo0cybN4+AgADq1asHlF72uHfvXgA2bdpEly5d6N69e6lZLIPBQO/evRkxYgTh4eHAtRm8wsJC43LFX3755ZbxIyMjGT58OM888wx9+vQxyZhq167N+fPnyz2Wm5tLZmYmPj4+tGjRwji+bt26sX37di5fvoyTkxPu7u6kpaVx7733AhAdHY3BYCAkJMQkfRQRERERMSdXV1euXr1aquz6+xo1apQqd3FxITc3t1RZbm5umXq/hx6ufRdcXFxo27Yt3bt3N5bFxcWVqnPx4kWOHDnChAkTKCws5MqVK/z1r3811r148SIjR46kQYMGABw+fJjw8HDCw8NZu3Ytn332Ge+++2658adMmUL79u25fPkyo0aNYvny5djZ/e9p8FevXsXZ2fmuxpSenk7dunXLPbZlyxbOnj3LqFGjyMzMxMnJCT8/P4KCgvj444/Jzc01XgtPT0/Onz/Pfffdx/jx4xk/fnyZ++lERERERKxB8+bNyczMJCMjwzhZk5KSgre3N25upR/J/ac//YlDhw6VKjt+/Dh//vOfTdYfzbSZ2Pr16wkNDSUmJoYFCxbg4+PD0aNHjcc9PDyYOHEikZGRALz77rvGJZTNmzenqKjotjFq1KiBk5MTAO7u7hw/fhyA77//nubNm99Vf1esWEFAQEC5xxITE5k9ezYxMTF89dVX7Nq1i6KiIry9vcnMzGTbtm0EBgYC0KtXL2JjY8nLywOufagvX758V30REREREakKmjRpQrt27YiMjCQ7O5tTp04xe/ZsBg4cWKZu3759SU5OZs2aNRQWFrJmzRqSk5N58sknTdYfzbT9TjfOJj300EPs37/fmJABPP7448ZNPK4LCAhg3rx57Nu3jxkzZjBp0iQcHBxwc3Nj+vTpt4wVGRmJu7s7hYWFDBs2DIAJEyYwZcoU7O3tcXV15b333itzXnBwMHAt2Xv77bfZtm0bBoOBoqIi7r//fnr27FnmnPz8fE6cOIGvry8ADg4OtGnThu+++45HH32U9u3b8/333xv/paFZs2YMGTKE0aNHG2f/pk6dekfXUERERESkqpk5cybTp08nMDAQe3t7+vXrx7hx4wDw8/MjIiKCvn374uvry6xZs/jggw+YOnUqDRs2JCoqiqZNm5qsL3YlJSUlJmtN5C4UnDPvIwGcvK/t/vPrrj1mjVvjEX8ur771Dp2VoVbfXgDkHUsxa9xqzX3J/yXVrDGd770HgCsJiWaN6/5ED3IPmW5XqDtR/f6WABzr1MOscZtvTyQrK8usMa//g9DF2FvvqFsZPEYOt9jPqyWu8bFHy/6jXWVqvm0tAOmffGbWuJ4Txpr9+wrXvrc5yd+bNabrw+3M/lmCa5+ny/9aZ9aYtfoEmf1vOlz7u54e9blZY3qGXbvVxhJ/66oqU33Ob17qWFVppq0KmjhxIhkZGcb39vb2LFiw4A8XU0REREREbk9JWxX00Ucf2URMEREREZHfosDBydJdMCttRCIiIiIiIlKFaaZNRERERESsiq3tyqGkTURERERErEqxjWVtWh4pIiIiIiJShWnLfxERERERsSrns3JM0k59N1eTtFPZtDxSRERERESsiq3NOylpE4ux2MN6c3LNGtfDtbrFxmqJuBpr5cYEjbWy41pqrDb10HQb+D0M18arsVYeWxsrWOZnp6rSPW0iIiIiIiJSZWimTURERERErIqNTbQpaRMREREREetia/e0aXmkiIiIiIhIFaaZNhERERERsSrF2NZMm5I2ERERERGxKra2PFJJm4iIiIiIWBVt+S8iIiIiIiJVhmbaRERERETEqhQX29ZMm8WTtvXr17NgwQIAfH19efXVV/nLX/5Cq1atKCgooFWrVkybNo3CwkLeeustfvnlF0pKSnj++efx8/Mrt83p06fzww8/8MADDzB16lQuXbrEhAkTyM/PZ+zYsXTp0qXc85KSkoiJiaG4uJjGjRszffp0Vq9ezTfffENMTAwA8fHxAOzatYuzZ89y+PBhWrVqhY+PD++9916ZNn/66SdmzJgBQJ06dYiIiKBGjRocP36ct99+m/z8fFxcXIiIiMDHx4ecnBymT5/O6dOnKSgo4KWXXuKhhx7CYDAQFxcHwKlTp+jTpw9JSUnUrl2b1NRUoqOjeeeddyq81hcuXGDatGlcvnyZkpISpk2bRvPmzY1tGwwGY90rV64wYMAAQkJC7qhPALt37+aVV16hYcOGxrKYmBicnZ0r7JeIiIiIyN2wsdWRlk3aTp48yfLly5k3bx7Ozs6sXLmS6OhoWrVqZUwGJk+ezE8//cTZs2epX78+b7zxBtnZ2YwcOZKvvvqqTJs//fQTOTk5LF26lFdffZUTJ07wzTffMGrUKB555JFbJm1paWksWLDAmGRER0ezbNkyXFxcOHjwIN9++y2BgYHG+tcTtJsTl5vNmjWLGTNm4O3tzT//+U8WLVrEqFGjeOONN/jwww/x8vLi+PHjTJ06ldjYWP7xj3/Qo0cPunbtyqVLl3jmmWeMieJ1a9eupW/fviQmJjJkyJA7vt4RERGEhobSqlUrTp48SXh4OEuXLi1V5/pYCgsLCQkJISQk5I76dF3//v0JCwu74z6JiIiIiEjFLHpP25o1a3j22WeNMzH9+/dn+/btperk5+dTo0YN3Nzc2Lp1K99//z2urq4sWrSo3Dbvu+8+ZsyYQXFxMRcvXsTZ2ZlDhw7Rrl07qlWrRs2aNbl8+XKZ87Zu3UqvXr2MfXnuuecYOHAgACNHjiQ6Opr8/Py7HmPt2rVZvHgxZ86coW/fvowePZr9+/fTrl07vLy8AGjWrBk1a9YkNTWVAwcO0LVrV+DazNyiRYtwdCydW+/YsYPnn3+exMTEO+5Hbm4uly5dolWrVgA0btyYOXPm3LJ+dnY2BQUFAHfUJxERERERcykpKTHJy1pY9P+8z549S48ePUqVVa9encOHD2MwGLhy5Qqenp54enri5eXFM888w4cffsgvv/zC888/z+DBg8tt18HBgWeffZbc3Fzq1avHr7/+So0aNQBwcXEhJyeHWrVqlTrnwoULxoQGoFq1asavPT096dmzJ/Pnz6devXp3NcZJkyYxc+ZMDAYDdevW5d133+XcuXP4+PiUqteoUSPOnz9fJhlyd3cv9f706dPUq1ePevXq4eDgQFpa2h314/Lly9StW7fCtuHazOH1ZHfq1KkAt+3TjVauXElycjIATZs2Zfr06XfUPxERERGRO2Vrz2mz6Eybh4dHmaSjuLjYuDxy1apV+Pv7Ex8fz/Hjx+nUqRNLlixhxYoVxMXFkZube8u258+fT1BQECtXrsTV1ZWcnBwArl69akzgbuTp6cm5c+eM7zMzM9mzZ4/x/bPPPsvatWvJyMi4qzEePnyY8PBwvv32W0aMGMFnn31W7rjPnTuHp6cnhYWFpcp37tzJr7/+anyfmJjI8ePHGTVqFGfPnuWbb765o37UqVOHCxculCpLSkqiqKioVFlcXBxxcXGUlJTQoEEDgNv26Ub9+/c3tqGETURERETk97No0tazZ09iY2ONyw6//vpr/P39S9Xx9PSkqKiI7du38+WXXwJQr149ateuTXFxcZk2Dxw4wOzZs4Frs3YA999/P3v27CEvL4/MzMxyZ4oeffRR1qxZY+zLwoUL+eGHH4zHnZ2dCQ0NNW5IcqfeffddTp06BUDz5s0pKiqiXbt27Nixw5i4HT16lPPnz9OoUSPatGnDpk2bAMjIyCAyMrLURh6bNm1i0aJFxMTEEBcXx9q1a++oH87OztSpU8c4ppSUFD777DMcHBzK1PXw8GDixIlERkYC3LZPIiIiIiLmpOWRZtSiRQv69euHwWDAzs6OZs2a8dprr9GxY0cMBgMODg5Uq1aNf/zjHzg5OREREcGAAQNwdXVl6NChuLq6lmnzz3/+M4sWLcJgMFCnTh3ee+89cnJyeOmll/jkk08YN25cuX2pX78+Q4cOJSQkBLh2n1loaCirVq0y1unevTtLliy5qzHOmDGDSZMm4eDggJubG9OnT8fZ2Znp06czefJk8vPzqVmzJm+//TYAEyZMYMqUKcTGxlJQUMC0adNwcnICrm2WUq1aNdzc3IBrs2eurq7k5+ezefNmgoODAejTpw8jRowo05epU6cyZcoU8vLyAHjrrbdu2e+AgADmzZvHvn37btmn8+fPG2M2bdqUwYMHl1oeCRAZGUmjRo3u6pqJiIiIiFTEmhIuU7ArsbURS5WRlZVl1njXk92LObdeVlsZPFyrW2ysloirsVZuTNBYKzuupcZ6rFOP29Q0rebbE/V7uJK5ublprJXI1sYKlvnZqap+OJNuknZaN/A0STuVzaq3AIyPj2flypWlysaOHUvHjh0rPG/OnDls27atVNmrr75KixYtfnNf0tPTefHFF0uV3X///bz88su/uc3f6vDhw8aljdcFBAQwevRos/dFRERERER+H6tO2oKDg43L8+7GmDFjGDNmjEn74unpWeHz2szpxufciYiIiIj80djaYkGrTtpERERERMT22FrSZtHdI0VERERERKRimmkTERERERGrUmxjM21K2kRERERExKrYWtKmLf9FRERERMSq7D15xiTt+DVuYJJ2KpvuaRMREREREanCtDxSLObXnclmjVejw8MAnP8gyqxx608K42LcMrPG9DAMASBr/SazxnV7vBvnP4w2a8z6L40HICVogFnj+q5bYbHv65WERLPGdX+iBwXn0swa08nbC4C8YylmjVutuS8XF3xp1pgezwwFLPMgcUt8lsAyv4d/DjaYNSZA0/g40j+aZdaYnhNDLfbA6V9GjDNrzHvnzebcm++ZNSaA92t/t8hnGOBY515mjdt86xqzxrsbtrY8UkmbiIiIiIhYFRvL2bQ8UkREREREpCrTTJuIiIiIiFgVW9tLUUmbiIiIiIhYFVu7p03LI0VERERERKowzbSJiIiIiIhV0fJIERERERGRKszGcjYlbSIiIiIiYl10T5uIiIiIiIhUGVY/0xYfHw9AcHAwAC+//DLjxo0jNDSU1atXY2dnB8Drr7/OoEGDeO+994iLi8NgMDBkyBCeeOIJAAwGA3FxcZSUlBAdHc22bdtwdnamZcuWhIeH4+TkBEBISAiPP/44w4cPN553XUFBAVOnTuWBBx64bd0rV64wYMAA/vKXvxAREcGVK1fIysqiYcOGDB48mK+++ormzZvz+uuvA7B//34GDx7M0aNHS7UD8MQTT9CkSROmT5/OqlWrcHR0ZPfu3SQnJ3P16lX++9//cvjwYVq1aoWTkxOxsbF8/PHHHDhwgOLiYoYPH0737t1veY0/+eQTTp48yT/+8Q+Kioro169fude2cePGTJ8+ndTUVOzt7XnyyScZMmTIb/m2ioiIiIjcku5p+wOwt7fHz8+P/fv307ZtW4qLi/nxxx+NydR1c+fOJTAwEBcXF2PZ8uXLKSws5KuvvgLg888/Z8OGDfTq1Yv09HS8vLzYvHmzMREDiIuLA+DEiRN89NFHfPLJJ7etW1hYSEhICCEhIcTFxRmTrLCwMAC++uorfvrpJ+N5SUlJeHt7l2nnut27d5ORkcGSJUsICQkxlv/9738H/peUXu9neno6sbGxFBYWMnjwYAICAoyJ6c0OHjyIo6Mj2dnZ1KxZ85bX9u9//zv9+vWjU6dOFBYWEhYWRkBAQKl+i4iIiIj8Xloe+QfRo0cPNm3aBMDevXtp165dmTqDBg1izpw5pcoSEhJ47rnnjO/HjBlDr169AFi/fj0BAQG4ublx6tSpMu3l5OTg6up6R3Wzs7MpKCiocAzNmjUjJSUFgF9++YV77723wvoDBw5kxYoVXLx4scJ6Li4u7N+/nx07dlBSUsLSpUtvmbAdP34cHx8fOnXqxPr164Hyr21+fj6pqal06tQJAEdHR2bPnq2ETURERETkd/rDJm3t27dn7969AGzcuJGePXuWqTN06FC2bt3K6dOnjWVFRUXUrFkTgPHjxzNs2DDeffddADZt2kSXLl3o3r07a9asMZ5jMBgICQlh1qxZhIaG3rZu7969GTFiBOHh4RWOoUuXLmzZsoVz587h4+NT6pjBYDC+ro+zevXqjBkzhk8++aTCdr28vJg6dSqxsbF07tyZuXPn3rLuunXreOyxx0qNo7xre+nSJWOCdubMGQwGAwMGDGDdunUV9kVERERE5G4Vl5SY5GUt/pDLI+HaTE/jxo05deoUR48eLTdBcnBwYNKkSbz33nvGMmdnZ/Ly8qhWrRrR0dGkpqYSHR3NxYsXOXLkCBMmTKCwsJArV67w17/+FSi7VPF2dS9evMjIkSNp0KBBhWN4+OGHWbx4Ma6urnTp0oX9+/cbj5W3PBKgd+/eLF26lKNHj96y3VOnTuHr60tMTAxXrlxhzJgx9O3bl0aNGpWpu2nTJr7//nvs7e05dOgQGRkZ1KtXr8y1zc/P59KlSwA0aNCAuLg44uPjycnJqXCMIiIiIiJ3y9buafvDzrTBtWV8X3zxBa1bt75lnQ4dOlBUVMSPP/4IQFBQEJ999pnx+N69e7Gzs2P9+vWEhoYSExPDggUL8PHxuWVidLu6Hh4eTJw4kcjIyAr77+zsTPXq1fnuu+/w8/O743G/8sorfPrpp7c8fvToUaKjowFwd3fH29ub4uLiMvV+/vlnGjduzLx584iJiWHs2LGsXbsWKHttnZ2d8fT0ZMeOHQAUFxezb98+42YlIiIiIiKmUlJSYpKXtfhDJG3R0dEEBwcTHBxcaqljhw4d2LBhQ7lLI28UHh5unBEaNGgQ1apVY9iwYRgMBmMCtmHDBgIDA43nPP744yQkJJTb3p3UDQgIIDs7m3379lXYt44dO1K9enUcHBxKld+4PHLmzJmljrVu3brC3SADAwOpXbs2/fv3x2Aw4OfnR+PGjcvUS0xMpGvXrqXG8c033wDlX9vXX3+dhIQEDAYDTz/9NNWrVycoKKjC8YmIiIiISMXsSqwpxZQ/lF93Jps1Xo0ODwNw/oMos8atPymMi3HLzBrTw3DtUQtZ6zeZNa7b4904/2G0WWPWf2k8AClBA8wa13fdCot9X68kJJo1rvsTPSg4l2bWmE7eXgDkHUsxa9xqzX25uOBLs8b0eGYoAFlZWWaN6+bmZpHPEljm9/DPwYbbVzSxpvFxpH80y6wxPSeGmv2zBNc+T7+MGGfWmPfOm825N9+7fUUT837t7xb5DAMc69zLrHGbb11z+0oWsvHgcZO00/3PzUzSTmX7w97TJndn69atZTYkGTBgAP369bNQj0REREREymdr805K2gSAzp0707lzZ0t3Q0REREREbqKkTURERERErIpm2kRERERERKqwYmwraftD7B4pIiIiIiLyR6WZNhERERERsSpaHikiIiIiIlKFFdtWzqbntImIiIiIiHX5138Om6SdPg+2Mkk7lU0zbWIxlnpYryUe6n15tXkfTlmr77WHb1riwcT5J0+ZNaZz40aAZR44nXvoiFljVr+/JQDHHu1p1rjNt621yIOfAS7OX2LWuB7PDrPYz6slrvGxTj3MGrP59ms/p+lRn5s1rmfYX83+fYVr39tfd+0xa8waj/hb7OHalvg9bO6/6XDt77olPsNgmb91UjUoaRMREREREatia4sFtXukiIiIiMj/Z+/O47Iq8/+Pv1BZZClccMmlMvfGSYLRFBJcChAzZTLcbnNcyElTa0wtp9QsHUwbE9TGGSy73RecKTd0RDQdkiykaNyr74QiYGiBimz37w++nK8IuA2dG36+nz3uR9zXOed6X+dwW4+P13XOLTWKzWarktedunz5Mq+++ipdu3bFx8eHqVOncunSpZsel5mZSffu3YmNjb2tPBVtIiIiIiIit2HOnDmkp6cTFxfHrl27SE9PZ8GCBTc8pri4mClTpnDhwoXbzlPRJiIiIiIiNUoxtip53YkrV67wySefMHHiRDw9PWnQoAFTpkwhNjaWK1euVHrckiVLaNKkCU2bNr3tTN3TJiIiIiIiNcovfU9bXl4eGRkVPzTvypUrFBQU0LZtW6PtoYceIi8vj++//54OHco/kfKzzz5j27ZtbN68maeeeuq2x6OiTUREREREapRf+jkkKSkpjBgxosJtkyZNAsDV1dVoq1u3LkCF97X9+OOPvPbaayxevBg3N7c7Go+KNhERERERkWt07dqV48ePV7jt3//+N++99x5XrlwxirDSZZHu7u5l9rXZbEydOhWLxcKvfvWrOx6P7mkTEREREZEapdhmq5LXnXjwwQdSOkUKAAAgAElEQVRxdHTk1KlTRtvp06dxdHTkgQceKLNveno6SUlJLFmyBF9fX3x9fTl79iyzZ8/m+eefv+VMzbSJiIiIiEiNYs/vaatbty4hISEsWLCA9957D4AFCxbQr18/XFxcyux733338fXXX5dp69WrFxMmTCAsLOyWMzXTdo0pU6awZ88e4/348eP51a9+hcViMV7r1683to8YMYJVq1YZ76/d7+mnn+ajjz4C4Ntvv2X06NGMHj2aKVOmVPodDlFRUTz99NMMHz6c3/72txw9etTo91ql76//d6m0tDT8/PywWCwMHjyYOXPmVHrOUVFR9OvXz3hfUFCAj48Phw4dAmD16tUMGzaMwYMHs2jRImw2W6X9X9te+jp79myl2SIiIiIiNdHMmTN54IEHeOqppwgODqZ58+a88cYbxvbQ0FDef//9KsvTTNs1pkyZwu9//3sCAgI4fPgw99xzD97e3lit1nL7ZmVl0bhxY/bu3cvw4cON9tJ9CwsLGTFiBCNGjGDJkiW8/fbbNGnShL///e+sWrWq0unQ1157ja5du/Kf//yHRYsW8e67797RuTz++OP86U9/AkoeL7p9+3b69u1b4b55eXmcOXOGZs2acfjwYWMt7qFDh0hNTcVqtVKrVi2io6PZsGEDfn5+Ffb/61//uky7iIiIiMgvwZ4zbVBy79qcOXMqnRzZtm1bpcfGx8ffdp5m2q7RpEkTevXqxebNm/nLX/7CH/7wh0r33bVrFwEBAXh4ePDDDz+U256bm0tBQQEAnp6erF69mrNnz9K/f3/GjBlz07FcvnyZe+65585P5hqDBg3iwIEDlW4PDAxk3759AOzbt4+AgAAAPvnkE8aNG0etWiUfk5EjR7J169bb7l9EREREpCrZ8542e9BM23XGjBnDwIEDGTx4MA0bNgTKLj98+eWX8fb2Jj4+3ljDun37dmPmzGKxkJ2djZOTEzNmzABKZvAWL16MxWKhQYMGREZG8uCDD1aYP3fuXDw8PDh58iRLly6tknPy9PQkMzOz0u1+fn5s2bKFoUOHcv78ee6//36g5MbJa7/8z93dnaKiohv2/+mnnxrXy93dnWXLllXJOYiIiIiI3K1UtF2nbt26dO7cmT59+hht1y+PzM7O5tixY0yaNInCwkJ+/vlno2izWq1kZ2czatQo7rvvPgCOHj3KtGnTmDZtGjt27OD9998nMjKywvzS5ZE//fQTo0ePZtOmTTg4OBjbr1y5gpOT022dU1ZWFg0aNKh0u6urK0VFRRw9epRWrVoZhVn9+vXJyMigRYsWQMkySkdHxxv2r+WRIiIiIvJLq0mzZFVByyPvwK5duxg/fjwxMTGsXLmSpk2blvkeh/r16/PSSy8xd+5cACIjI40llG3atKlwtup6bm5uRoF0zz33GI8U/eKLL2jTps1tjXfz5s3GksfKdO3alQULFtCrVy+jrV+/fixdupTi4mIAli1bRnBw8B31LyIiIiJSVWw2W5W8agrNtN2Ca5dH/uY3vyElJcUoyACefPLJcvd6BQQE8MEHH3DkyBHefvttpkyZQu3atfHw8ODNN9+sNGvu3Lncc889FBYWMnToUKDkW9dfe+01atWqhaurK/Pnzy93XOkjQ93c3Jg3b56xTLGoqIiHH36YkJCQG55jz549WbFiBe3bt2f37t3GOXz//fcMHjwYKCnswsPDOXv2bIX9nzlzpszySPi/5aQiIiIiIlWluObUW1VCRVsFrl3eV9GTI683YMCACts//PBD4+drvyqgMi+++CIvvvhiufY2bdqwYcOGcu2lY6tojAcPHrxpXmlmqYSEhHJtzz33HM8991yZY5o3b15h/5W1i4iIiIjInVPRZicvvfQS58+fN97XqlWLlStX/n+XKSIiIiJS1WrS0saqoKLNTv785z/fFZkiIiIiIlXtbiva9CASERERERGRakwzbSIiIiIiUqPcbY/8V9EmIiIiIiI1yl1Ws6loExERERGRmkX3tImIiIiIiEi14WC728pUERERERGp0Zbv+axK+ono/ViV9PNL0/JIsZuT/kGm5rU5EAdA3rETpua6tG9LTk6OqZkeHh4AXD6cbGquq683BWfPmZrpeF8TAK4c+drU3LqdO1FwLsPUTMcmjQHskmuvz7A9cguzzt98xypUx6shYJ9ztdtnOO2MubnNm5H/7femZgI4tXqAq8dPmZrp3K616Z8lKPk85f8nzdRMp5bNKTiTbmomgGOzpqbnOjZrCsClg4dMzXXz62pq3u242+adtDxSRERERESkGtNMm4iIiIiI1Ch65L+IiIiIiEg1drcVbVoeKSIiIiIiUo1ppk1ERERERGqUu+1BJCraRERERESkRrnLajYVbSIiIiIiUrPonjYRERERERGpNjTTJiIiIiIiNYruaRMREREREanGVLSZJDY2lm3bthETE2O8B3B3d2flypUAPPTQQ/zxj3/EycmJTp06sX37dlq0aAGAxWLBarUa/y516NAhkpKSyM3NpXv37gQEBACQnJzMJ598Qr169ejSpQtnzpypMD8sLIykpCQWL16MzWbDxcWF1157jYceegiAjRs3smnTJtavX28ct3z5cry8vCgoKKBHjx688MILN923sLAQV1dXli1bxh//+EfS09M5evQoHTp0oGnTpjz22GPMnTuXzz77jDp1Sn5NISEhjB07FsDoB+Dee+8lOjqaXr16sWDBAh599FHjGr3wwgssXbqUrKwsALy8vHjhhRdwdnZm8eLF1K5dm/vvv5/XXnvNyLneDz/8wFNPPUVCQgKenp7Mmzevwmv7xhtvsHr1av7xj3/g5OTEfffdxxtvvIG7u/sdfUZERERERMTO97SlpqayZ88e431RURGbNm3igw8+YPXq1Xh7exMdHQ2Aq6sr8+fPv+W+g4ODy/S9e/dugoODb5gPcOHCBRYtWkR0dDSrV69m5syZLFq0yNiekJDAAw88wHfffWe0RUREYLVaWbt2Lf/617+4dOnSLe3buXNnkpKSmD9/PlarlQ4dOmC1Wo3z9PT05MsvvwRKCqfLly+X68dqtRrXyM3Njfnz55f5m4du3bphtVqJiIgwjunWrRtLlixh2bJlxMTE0KBBA3bu3FnptdyxYwf9+/cnLi7uhtc2MTGRlJQU1q5dy6pVq+jRowdr1qyptF8RERERkTtRbLNVyaumsGvRNmrUKKKjo8nPzwfg/PnzjBw5EicnJwAGDhzIgQMHAGjbti0ODg4kJibeUt+dO3fm2LFjRgHz1Vdf4evre8N8gH379hEaGoqnpycALVu2ZPHixQD8/PPPFBUV0bdvX7Zu3Vous6CgAJvNRp06dW66L0BGRgaOjo6VnkNgYCD79u0DID4+np49e97wnD09PfH29mbz5s033A/A0dGR9evX8+OPPzJu3Dj69u1b6b4HDx5k4sSJRtFW2bX9+OOPGTt2LLVr1wagX79+RERE3HQsIiIiIiK3w1ZFr5rCrkWbl5cXISEhfPjhhwAsXbqUJk2alNnHxcXF+Hnq1KksXLiQoqKim/bt4ODAI488QmpqKidOnKBdu3bUqlX2dK/PBzh37hwtW7YEYP369VgsFkJCQgDYs2cPgYGBdOvWjYSEBOOY5cuXY7FYGDt2LEOHDsXZ2fmG+4aHh/P444/TrFkzunbtWuk5dOzYkWPHjgEls4KdOnUql2mxWIyiEmD8+PF89NFH5Obm3vD6zJ07l2+//ZYBAwYQERFBdnZ2hfudOXOGhg0b0rBhQ2rXrk1GRkal1zYjI4P7778fgFmzZmGxWHjppZduOA4RERERkdt1t8202f1BJCNHjiQ8PJyQkBDq169PRkYGrVq1MrYXFxcbPzdv3hx/f3/WrVt3S30HBQURHx9PnTp1yi2NrCi/YcOGeHl5kZmZCUB4eDjh4eFYLBYAdu3aRW5uLrt37yYjI4PU1FSgZKliWFhYmX5vtu9bb72Fq6vrTc+hWbNmnDhxAjc3NxwcHIz2ijKh5J5Ai8XCkiVLKu2zoKCAtLQ03nzzTWw2GytWrGDNmjVMnDix3L5xcXGcOnWK0aNHk5GRwbZt2xg1alSF19bLy4uMjAxatGjBrFmzAIxrJyIiIiIid8bu39Pm5OTE+PHjiYmJYcyYMaxYscJYrrhx48ZySxqff/5548EeN+Pj48PRo0f55ptv8PHxuWk+QM+ePYmNjeWnn34CSpYwnjlzhtzcXHJycrBarcTExDBr1qxKlz3eyr5Tpkxh/fr1RoFYmYCAACIjI/H397+lcwb47W9/S3JyMhcvXqxwe61atfjjH/9IdnY2Dg4OtG7dutLZy/j4eFatWkVMTAxWq5UdO3YAFV/b/v37s2TJEqPQLr0fT0RERESkKtlstip51RR2n2kD6NOnD2vWrMHNzY0BAwZgsViMYuL1118vs2/dunUZO3YsGzZsAEqWDZbOOHXt2pXAwEBjXwcHB+Npk9cvjawoH6B+/fr84Q9/YNKkSRQXF1NUVMS0adPYu3cvfn5+xjH+/v688847tG7dulzft7Kvi4sLERERLFmyhNmzZ1c6tu7duzN9+nSio6ONgglKlkdu2bLFeP/Xv/7V+LlWrVq88sorvPLKKxX2Wbt2bV5//XXGjBmDq6srDRs25K233iq3X0ZGBs7Oznh4eABQr149XF1d+fbbb2nVqlW5a+vn58fZs2exWCzUqlULZ2dnZsyYUem5iYiIiIjcieLimlNwVQUHW00qMeX/Kyf9g0zNa3Og5EEqecdOmJrr0r4tOTk5pmaWFtqXDyebmuvq603B2XOmZjreV3If7JUjX5uaW7dzJwrOZZia6dikMYBdcu31GbZHbmHWeVMz63g1BOxzrnb7DKedMTe3eTPyv/3e1EwAp1YPcPX4KVMzndu1Nv2zBCWfp/z/pJma6dSyOQVn0k3NBHBs1tT0XMdmTQG4dPCQqblufpU/e8He5v19z813ugWvDuhdJf380qrFTJtUD9fff9aoUSMWLlxop9GIiIiIiFTsbpt3UtEmhmu/pFxEREREpLqqSU9+rAp2fxCJiIiIiIiIVE4zbSIiIiIiUqPcXfNsKtpERERERKSG0T1tIiIiIiIi1djddk+bHvkvIiIiIiI1yqxNcVXTzzPmfgXVndJMm4iIiIiI1Ch327yTijaxG3t8uSrAhTUbTc2tN3QQaROnmZrZfHEkYJ8v4bTX7zVj7rum5jZ+7WUuJSaZmunWrQsAWe+9b2qu16Rxdvu9Xj152tRc5zYPkTbhFVMzm0e/A0D25TxTc+u7upAV9RdTM71efB6Ak/7m/s12mwNxnHsz0tRMgCZvTOPihi2mZno+O9BuX659dvosUzPv+9MscnbFm5oJ4PFkL04+HmJqZptPdwCQOX+xqbmNpk40Ne923G3LI/XIfxERERERkWpMM20iIiIiIlKj3GUTbSraRERERESkZtE9bSIiIiIiItWY7mkTERERERGRakMzbSIiIiIiUqPcbTNtKtpERERERKRGudvuadPySBERERERkWpMM20iIiIiIlKjaKZNbktsbCzBwcFYLBYsFgt/+9vfiI2NLbNPfn4+s2fPxmKxMGTIEHbs2GFs27VrF8OGDWPYsGG88cYb5OfnA+Dr62v02b9/f7744gsA/vnPf/Lcc88xatQooqKijDH4+vpSWFho9BsSElJmHE888QR79+41xhMWFkZmZiYA27dv59133630HBMTE+nRowdFRUUATJgwgVOnThnbt27dSkxMDDabjaioKJ599lmGDx/OW2+9RUFBwe1fVBERERGRGyi2Vc2rplDRVgUiIiKwWq1YrVbq169fbvv7779Pp06dsFqtrFy5ki1btnD69Gn+53/+h02bNvHBBx+wevVqvL29iY6OBqBDhw5Gn0uXLmXNmjUArFy5kg8++IAVK1aQnp5OcnIyAJ6ennz55ZcA/PDDD1y+fNnIT0lJ4bHHHmPbtm0AODk5MXnyZCIjI7l48SJWq5Xx48dXen47d+4kMDCQxMREAIKDg4mPjze2//Of/yQoKIhNmzZRWFjIhg0bWLVqFV5eXuzevfu/ubQiIiIiIuXYbLYqedUUKtpM8PnnnxMWFgaUFExDhgxh+/btbN++nZEjR+Lk5ATAwIEDOXDgQLnjMzIycHR0BODy5ct8/PHH5OTkMHv2bDp37gxAYGAg+/btAyA+Pp6ePXsax+/cuZOwsDAyMjKMYq5Hjx4UFBQwYcIEJk6ciLOzc4VjLy4u5vTp04wYMYKtW7caWQcPHgRKZu0uXrxI8+bN2bp1K2PHjjWOjYiIoG/fvnd+4UREREREREVbVVi+fLmxlPFWKvaWLVuSmZlJeno6TZo0KbPNxcUFgKNHj2KxWOjVqxfLly/npZdeAmDRokUcPHiQoKAgpk6dSl5eHgAdO3bk2LFjAKSmptKpUyejzyNHjtC5c2f8/f3LzJCFhoby/fff4+PjU+lYDx8+jLe3N61bt+bUqVNcvXoVd3d3PDw8+PHHHzl48CA9evQAoKioCHd3d6BkCeXQoUOJjIy86fUQEREREbkdmmmT23bt8kgHB4dy26+91wzg3LlzeHl5Ub9+fTIyMspsKy4uBv5veeTChQv56aefaNCgAbm5ueTl5fHOO++wf/9+GjRowPbt241jmzVrxokTJ3BzczPGkZqaSnp6OmPGjGH//v3GbFl+fj4xMTH079+fFStWVHpucXFxJCUlMXr0aC5evEhCQgLwf/fIlS6NhJJZxKtXrwIQHR3NO++8w4ULF27nUoqIiIiI3FSxzVYlr5pCRZsJvL292bx5M1BSLK1YsYLg4GBCQkJYsWKF8fCRjRs34uvrW+7YRx99lDVr1pCfn8+rr77KlStXqFOnDg8++KBR5AEEBAQQGRmJv7+/0RYXF8fs2bOJiYlh9erVnD9/nosXL7JkyRIGDhzI5MmTiYuLIy0trdy4bTYbycnJrFu3jpiYGKKiooyir3fv3iQmJpKdnU2zZs2Aknvd3n//feP45OTkCotYERERERG5dSrafgHR0dGEhYURFhZGUlISkyZN4siRIwwePJjhw4czcOBA2rZtS7t27RgwYAAWi4XBgweTkpLCiy++WK6/cePGsXbtWpycnBgzZgxDhgzBYrFw7NgxBgwYYOzXvXt3vvrqK/z8/Iy2AwcO0K1bN+N9z549+cc//sHnn39OeHg4Tk5OTJkyhbfffrtcbnJyMh07djQKrw4dOvDdd9+Rk5ODu7s7V69epUuXLsb+gwYNwtnZmaFDh2KxWNi1a9cNH3AiIiIiInIn7rblkfqetv9S6QNGrn1/fRvAnDlzKjw+NDSU0NDQcu1Wq9X4+Z577jG+JiA4OJjg4OBKx/D555+Xabt+LKVF1HPPPWe0+fn5lSn0Sj366KM8+uijZdpKZ9oA40mXpRwcHBg3bhzjxo0r15eIiIiISFWpSY/rrwoq2gSArKwsXn755TJtDz/8MNOnT7fTiEREREREBFS0yf/y8vIqM7snIiIiIlJdFduKb77T/0dUtImIiIiISI1Sg25HqxIq2kREREREpEapSQ8RqQp6eqSIiIiIiMhtuHz5Mq+++ipdu3bFx8eHqVOncunSpUr337ZtGyEhITz66KMEBQWxdu3a28pT0SYiIiIiIjWKvb9ce86cOaSnpxMXF8euXbtIT09nwYIFFe574sQJZsyYwbx58/jyyy+ZN28eb7/9NocPH77lPBVtIiIiIiJSo9jze9quXLnCJ598wsSJE/H09KRBgwZMmTKF2NhYrly5Um7/77//nsLCQoqLi7HZbDg4OFC7dm2cnJxuOdPBdrctCBURERERkRpt7F/WV0k/f30+vML2vLw8MjIyKtx25coVnn76ab744gvc3d0ByM3NxcfHh7///e906NChzP6XL18mIiKCzz//nNq1a1NUVMS0adMYNWrULY9TDyIRu7l6/JSpec7tWgNw8vEQU3PbfLqD3Pj9pma69+oBQG7CAXNzA/356ZOdpmbe+1TJl81nzl9sam6jqRPJ2bPP1EyP3gEAXE76wtRc1y4+FJxJNzXTsVlTAPL/k2ZqrlPL5nb5cwOQfTnP1Nz6ri5c+uzWl+ZUBbfHfAG4uGGLqbmezw40/b8RUPLfiZP+QaZmtjkQR05OjqmZAB4eHmS9976pmV6TxnFh7WZTMwHqDfktF9fHmprpGR4GQOaCKFNzG0150dS82/FLzzulpKQwYsSICrdNmjQJAFdXV6Otbt26ABXe15afn0/z5s154YUX+M1vfsPBgwd56aWXaNu2Lf7+/rc0HhVtIiIiIiJSoxT/wmsFu3btyvHjxyvc9u9//5v33nuPK1eu4ObmBmAsiyydebtWVFQUTk5OdO/eHYDAwEBCQ0NZv379LRdtuqdNRERERERqFHve0/bggw/i6OjIqVP/t2rs9OnTODo68sADD5Tb/+zZsxQUFJRpq1OnDo6OjrecqaJNRERERETkFtWtW5eQkBAWLFhAdnY22dnZLFiwgH79+uHi4lJu/169erF9+3Y+/fRTbDYbSUlJfPzxxzz11FO3nKnlkSIiIiIiUqMUY99nKc6cOZPIyEieeuopCgoK6N27N6+//rqxPTQ0lKeeeopx48YxaNAg8vLyeOutt8jKyuK+++5j1qxZ9OzZ85bzVLSJiIiIiEiNYu8H4Lu7uzNnzhzmzJlT4fZt27aVeW+xWLBYLHecp+WRIiIiIiIi1Zhm2kREREREpEYp/qUfH1nNqGgTEREREZEaxd7LI82m5ZEiIiIiIiLV2F1ftG3evJlhw4YRHh7Ou+++C8Dq1asZNmwYgwcPZtGiRdhsNtLS0mjXrh1ff/21cezMmTOZPn36DbcBnDp1itGjR2OxWIiIiCA9PR0ouSFx69atxjHX3pyYmJhIjx49KCoqAuDQoUP06tULi8VijC07O7tMe+krPz+/wj4qMn36dJ5//nnjfVZWFu3btyctLY3i4mKioqIYPnw4gwcPZtWqVeXGMmjQIJYvX16u/fqxiIiIiIhUlWJb1bxqirt6eeQ333zDwYMHsVqt1KpVi1dffZWEhARSU1ONtujoaDZs2ICfnx8tWrRg3759dOrUCYDjx48bX6BX2bbCwkJmzZrFwoULady4MadOnWLGjBmsWLECgL/+9a/07t2bunXrlhnbzp07CQwMJDEx0fim9IEDB/Liiy8CEBsby+7du3nggQfKtN+sj4qcOXOGq1ev4uzsTEJCAo0bNwZgy5Yt2Gw2Vq1ahc1mY8aMGRw4cABHR8cymTNmzCAlJaXcGEVEREREfglaHnkX2bVrF4MGDaJWrZLL8PrrrxMXF8e4ceOMtpEjRxqzYd7e3hw5cgSAY8eO0b59e6OvyralpKTg4+NjFEKtW7fG3d2dtLQ0gDIzVaWKi4s5ffo0I0aMKDMTd62MjIwbfov6rfRR6rHHHuOzzz4D4IsvvsDX1xeA7du3G7NwDg4OZa7FtZ555hkOHDhwwwwRERERkapiq6J/aoq7umjLzs6mUaNGxntXV1cyMzNp2rSp0ebu7m4sL6xduzb16tUjOzubvXv3lvlCvMq2nTt3rkx/UDIrl5mZCcCQIUPYv38/Z86cMbYfPnwYb29vWrduzalTp7h69SpQMvM1dOhQ/P39uXDhAv369TPaS5cjvvHGGzfsoyKBgYHs37+f/Px8atWqZRSD+fn5ODs7G/u1bNnSGPe1PD09jfaKxiIiIiIiInfurl4e6eXlRXp6Og899BBQskzQxcWFjIwMWrRoAUBeXl6ZGa0ePXqwf/9+Tp8+zVNPPVWmv4q21a9fn1OnTpXZ79y5c3h5eQElxd6UKVOYP3++sT0uLo7U1FRGjx7NxYsXSUhIwNPT01h6uGLFCs6dO4eTkxNQ8ZLEivoICgqq8Do88MADfP/99xw6dIiuXbuSmJgIQK1atcjPzzdyrh33tbKysmjQoEGlYxERERERqUrFWh559+jTpw8bN26kuLgYm81GVFQUYWFhLF26lOLiYgCWLVtGcHCwcUyPHj3YunUrDRs2LNdfRdt8fHw4ePAgGRkZQMm9bpmZmUZRCNCtWzeKioo4ceIENpuN5ORk1q1bR0xMDFFRUeWWJP7ud78jJSWFo0ePVnhet9LH9Vq3bs1HH31Ejx49jLYnn3yS999/HyhZbrls2TL69u1b7tjY2FgCAgJu2L+IiIiISFWx2WxV8qop7uqZto4dO9KlSxeGDBlCcXEx3bt3p3fv3qSlpTF48GAAunbtSnh4OGfPngXg3nvv5cqVKxUWKRVtc3Jy4s033+SVV14hPz8fd3d35s2bV+7YadOm0bdvX5KTk+nYsSMODg4AdOjQge+++46cnBxjXwcHB1555RUWLlzI2LFj2bJlC0lJScb2yZMnV9qHh4dHhdciMDCQ1NRU6tWrZ7QNGTKERYsW8eyzz2Kz2QgNDSUgIIBDhw4ZmQUFBfTq1YtHHnmkTHupuXPnlilQRURERETk9jjYalKJKf9fuXr81M13qkLO7VoDcPLxEFNz23y6g9z4/aZmuvcqmTHNTTD3ATHugf789MlOUzPvfapkJjxz/mJTcxtNnUjOnn2mZnr0LvkLoctJX5ia69rFh4Iz6aZmOjYruRc4/z9ppuY6tWxulz83ANmX80zNre/qwqXPDpua6fZYyYOuLm7YYmqu57MDTf9vBJT8d+Kkf8W3JvxS2hyIK/MXvWbx8PAg6733Tc30mjSOC2s3m5oJUG/Ib7m4PtbUTM/wMAAyF0SZmttoSvW95SVs4QdV0k/sH35XJf380u7qmba7zbXfAwfQqFEjFi5caKfRiIiIiIjcmbtt3klF213EarXaewgiIiIiIv+1u61ou6sfRCIiIiIiIlLdaaZNRERERERqlLvtkf8q2kREREREpEa524o2LY8UERERERGpxjTTJiIiIiIiNTI1VOkAACAASURBVMrd9iASfU+biIiIiIjUKH3nLa+Sfra/GlEl/fzSNNMmdlNwLsPUPMcmjQHI2b3X1FyPJ3py5etvTM2s2+lhAPKOHjc116VDOzIXRpua2egPEwD7fOGoPb40FyDvm2Om5ro83N70L+v18PAAoDAjy9TcOo297Pbn1R7X2B6fJYBLiUmm5rp162K3L9e2x/9z7PXl2tnW9aZm1reEc37JX03NBGg4fqxdPsMA2R+uMTW3/sihpuZJ5VS0iYiIiIhIjXK3PYhERZuIiIiIiNQod9sdXiraRERERESkRrnbZtr0yH8REREREZFqTDNtIiIiIiJSo2h5pIiIiIiISDV2l9VsWh4pIiIiIiJSnWmmTUREREREapS77UEkKtpERERERKRG0T1tIiIiIiIi1VjCrAn2HoKpdE/bLyg2NpbY2NgybYmJifTo0YOioiIADh06RGhoKIWFhcb7qKgoDh06RK9evbBYLAwaNIjly5eX6WfEiBGsWrXKeG+xWIzX008/zUcffVSu3WKxsH79+kr7qMyrr75KZGQkABcvXmTw4MFlto8dO5bz589z7tw5xo0bx5AhQxg6dCh79uy51UslIiIiIiKV0EybyXbu3ElgYCCJiYn4+/sDcP78edasWcOIESPK7Dtw4EBefPFFAGbMmEFKSgqPPPIIWVlZNG7cmL179zJ8+HBjf6vVCkBhYSEjRoww+ittv1ZlfVwvPz+fn3/+mbNnz1JcXIynpydubm5kZmbSqFEjcnJyKC4upmHDhowaNYqpU6fSvn17Ll++zO9//3v8/PxwcXH57y6aiIiIiMhdTDNtJiouLub06dOMGDGCrVu3Gu3PPPMMmzdvJjs7u9Jjn3nmGQ4cOADArl27CAgIwMPDgx9++KHcvrm5uRQUFNxwLDfro9S//vUvfv3rX/Pwww/z+eefAxAUFMTevXsB2Lt3L7179+bs2bO4u7vTvn17AFxdXfnwww9VsImIiIiI/JdUtJno8OHDeHt707p1a06dOsXVq1cBcHFxISIigvfee6/SYz09PcnMzAQgPj6ewMBA+vTpw/bt2419LBYLoaGh/O53v2PatGll2ktfycnJN+zjenFxcTzxxBM88cQTbNu2DYA+ffqwf/9+o5+goCDOnTvH/fffD8BXX32FxWJhwIABHDly5E4ulYiIiIiI/C8tjzRRXFwcqampjB49mosXL5KQkICnpycAoaGhrFu3juPHj1d4bFZWFg0aNCA7O5tjx44xadIkCgsL+fnnn3n++eeBkmWQ2dnZjBo1ivvuu8849vrlkTfq41qFhYUkJiYaxeLJkycpKCigfv36FBUVceHCBfLy8mjQoAGXL1829vv1r3+N1WolKirKKExFREREROTOaKbNJDabjeTkZNatW0dMTAxRUVFllkhCyQM/li1bVuHxsbGxBAQEsGvXLsaPH09MTAwrV66kadOmZQq9+vXr89JLLzF37txKx3KzPkolJiYSGhpKTEwMMTExBAUF8emnnwIQGBjIokWLCAgIAKBFixacP3+eY8eOASX3wn3zzTc4ODjc3oUSEREREZEyVLT9wqKjowkLC6NDhw64ubkZRUyHDh347rvvyMnJMfbt2LEjffr0Md5v2bIFi8XC4MGDadWqFY888gi7d++md+/exj5PPvlkueIvICCA3NxcY2nitcsjFy9efEt9QElx16tXL+N9UFCQsd+TTz7JP/7xD5588klj+9y5c1myZAkWi4XnnnuOTp064ePjc0fXTURERERESmh55C8oLCyMsLCwSreXFkDXFmpz5swxfo6Pjy93TExMTJn3AwYMqLDvDz/8EKj4yZHXq6yPa8cC4Ovri6+vL1Ayo3f9/WqNGzcmKirqpnkiIiIiInLrVLQJULL8csuWLWXaxo0bh5+fn51GJCIiIiIioKJN/tfNZgVFRERERMQ+dE+biIiIiIhINaaiTUREREREpBpT0SYiIiIiIlKNqWgTERERERGpxhxsNpvN3oMQERERERGRiunpkWI3ecdOmJrn0r4tACf9g0zNbXMgjpzde03N9HiiJwC5CQdMzXUP9OfnrXGmZt7Tr+T3mRm5yNTcRtMmk7Nnn6mZHr0DALj02WFTc90e86Ug7YypmY7NmwGQ/z8/mJrrdH8Lu/1esy/nmZpb39WFS4lJpma6desCwIU1G03NrTd0EJnzF5uaCdBo6kROPh5iamabT3eQk5NjaiaAh4cHWYuWmZrpNfn3XFi72dRMgHpDfmuXzzBg+ue40dSJpuZJ5bQ8UkREREREpBpT0SYiIiIiIlKNqWgTERERERGpxlS0iYiIiIiIVGMq2kRERERERKoxFW0iIiIiIiLVmIo2ERERERGRakxFm4iIiIiISDWmok1ERERERKQaU9EmIiIiIiJSjalou4nNmzczbNgwwsPDeffddwFYvXo1w4YNY/DgwSxatAibzUZaWhrt2rXj66+/No6dOXMm06dPv+E2gFOnTjF69GgsFgsRERGkp6cDYLFY2Lp1q3GMxWIxfk5MTKRHjx4UFRUBcOjQIXr16oXFYjHGlp2dXaa99JWfn19hH5X54Ycf6Ny5MxcvXgRg3rx57Nu3z9ienJzMm2++aVybZ599luHDhzN16lRyc3Nv84qLiIiIiMi1VLTdwDfffMPBgwexWq2sX7+erKwsEhISSE1NxWq1sm7dOurUqcOGDRsAaNGiRZli5vjx48bPlW0rLCxk1qxZzJ07F6vVytSpU5kxY4ax31//+leuXLlSbmw7d+4kMDCQxMREo23gwIFYrVajcNq9e3eZ9tKXk5NTpX1UZMeOHfTv35+4uDgAgoOD2bNnj7F99+7dBAcHk5iYSEpKCmvXrmXVqlX06NGDNWvW3OQqi4iIiIjIjahou4Fdu3YxaNAgatUquUyvv/46cXFxjBs3zmgbOXKkMRvm7e3NkSNHADh27Bjt27c3+qpsW0pKCj4+PjRu3BiA1q1b4+7uTlpaGgCDBg1i+fLlZcZVXFzM6dOnGTFiRJmZuGtlZGTg6OhY6bndSh+lDh48yMSJE42irXPnzhw7dgybzQbAV199ha+vLx9//DFjx46ldu3aAPTr14+IiIgb9i0iIiIiIjemou0GsrOzadSokfHe1dWVzMxMmjZtarS5u7sbywtr165NvXr1yM7OZu/evfTs2dPYr7Jt586dK9MflMzKZWZmAjBkyBD279/PmTNnjO2HDx/G29ub1q1bc+rUKa5evQrAli1bGDp0KP7+/ly4cIF+/foZ7aVLI994440b9nG9M2fO0LBhQxo2bEjt2rXJyMjAwcGBRx55hNTUVE6cOEG7du2oVasWGRkZ3H///QDMmjULi8XCSy+9dAdXXkRERERESqlouwEvLy/j/jIoKWBcXFzIyMgw2vLy8srMaPXo0YP9+/dz+vRpHnrooTL9VbStfv36ZfqDkkLOy8sLKCn2pkyZwvz5843tcXFxJCUlMXr0aC5evEhCQgJQsgxyzZo1jBo1CsBYBnnt8sjSe88q6+N6cXFxxj136enpbNu2DYCgoCDi4+ONpZGl16v0XGbNmoXVauX8+fM3u8wiIiIiInIDKtpuoE+fPmzcuJHi4mJsNhtRUVGEhYWxdOlSiouLAVi2bJlRtEBJYbZ161YaNmxYrr+Ktvn4+HDw4EGj2Dl+/DiZmZm0aNHC2Kdbt24UFRVx4sQJbDYbycnJrFu3jpiYGKKiosotb/zd735HSkoKR48erfC8bqWPUvHx8axatYqYmBisVis7duwwxn306FG++eYbfHx8AOjfvz9Lliwxrs2XX3554wssIiIiIiI3VcfeA6jOOnbsSJcuXRgyZAjFxcV0796d3r17k5aWxuDBgwHo2rUr4eHhnD17FoB7772XK1euEBAQUK6/irY5OTnx5ptv8sorr5Cfn4+7uzvz5s0rd+y0adPo27cvycnJdOzYEQcHBwA6dOjAd999R05OjrGvg4MDr7zyCgsXLmTs2LFs2bKFpKQkY/vkyZMr7cPDw8PYLyMjA2dnZ6OtXr16uLq68u2339KqVSujsCy9v8/Pz4+zZ89isVioVasWzs7OZR6qIiIiIiIit8/BVvo0CRGT5R07YWqeS/u2AJz0DzI1t82BOHJ27zU10+OJknsmcxMOmJrrHujPz1vjTM28p1/J7zMzcpGpuY2mTSZnz76b71iFPHqX/IXPpc8Om5rr9pgvBWlnbr5jFXJs3gyA/P/5wdRcp/tb2O33mn05z9Tc+q4uXEpMuvmOVcitWxcALqzZaGpuvaGDyJy/2NRMgEZTJ3Ly8RBTM9t8uqPMX+SaxcPDg6xFy0zN9Jr8ey6s3WxqJkC9Ib+1y2cYMP1z3GjqRFPzpHKaaRPDtd8DB9CoUSMWLlxop9GIiIiIiAioaJNrWK1Wew9BRERERESuoweRiIiIiIiIVGMq2kRERERERKoxFW0iIiIiIiLVmIo2ERERERGRakyP/BcREREREanGNNMmIiIiIiJSjemR/2I3lw8nm5rn6usN2OcLp/O+OWZqpsvD7QH7fJF43tHjpma6dGgHQM6ueFNzPZ7sZbdzvXrytKm5zm0eojDrvKmZdbwaApj+JcEeHh52+/Nqj3O1x2cJ4NKBz0zNdfN/jJ937jE1E+Ce4N52+X+Ovb5cO3ffQVMz3QP8yP30X6ZmArg/3t0un2HA9M/xPcG9Tc2TymmmTUREREREpBpT0SYiIiIiIlKNqWgTERERERGpxlS0iYiIiIiIVGMq2kRERERERKoxFW0iIiIiIiLVmIo2ERERERGRakxFm4iIiIiISDWmok1ERERERKQaq2PvAdhbbGws27ZtIyYmxngP4O7uzsqVKwF46KGH+OMf/4iTkxOdOnVi+/bttGjRAgCLxYLVajX+XerQoUMkJSWRm5tL9+7dCQgIACA5OZlPPvmEevXq0aVLF86cOVNhflhYGElJSSxevBibzYaLiwuvvfYaDz30EAAbN25k06ZNrF+/3jhu+fLleHl5UVhYiKurK8uWLWPr1q1GO8C9995LdHR0hX1UJjExkWnTprF3715q167NhAkTmDx5Mq1btwZg69atZGRkMGrUKKKjo/n0009xcnKiffv2TJs2DUdHx//mVyQiIiIiclfTTBuQmprKnj17jPdFRUVs2rSJDz74gNWrV+Pt7W0UOq6ursyfP/+W+w4ODi7T9+7duwkODr5hPsCFCxdYtGgR0dHRrF69mpkzZ7Jo0SJje0JCAg888ADfffed0RYREYHVamXt2rV07tyZpKSkMu1Wq9U4j8r6qMjOnTsJDAwkMTHROKf4+Hhj+z//+U+CgoLYtGkThYWFbNiwgVWrVuHl5cXu3btv9VKJiIiIiEgFVLSBMUOUn58PwPnz5xk5ciROTk4ADBw4kAMHDgDQtm1bHBwcjALmZjp37syxY8ew2WwAfPXVV/j6+t4wH2Dfvn2Ehobi6ekJQMuWLVm8eDEAP//8M0VFRfTt25etW7dWmJuRkXHDGa5b6QOguLiY06dPM2LECGO/wMBADh48CEB+fj4XL16kefPmbN26lbFjxxrHRkRE0Ldv30r7FhERERGRm1PRBnh5eRESEsKHH34IwNKlS2nSpEmZfVxcXIyfp06dysKFCykqKrpp3w4ODjzyyCOkpqZy4sQJ2rVrR61aZS/79fkA586do2XLlgCsX78ei8VCSEgIAHv27CEwMJBu3bqRkJBgHLN8+XLCw8N5/PHHadasGV27djXaLRYLFovFKPwq6+N6hw8fxtvbm9atW3Pq1CmuXr2Ku7s7Hh4e/Pjjjxw8eJAePXoAJTOU7u7uAEyYMIGhQ4cSGRl502skIiIiIiKVu+vvaSs1cuRIwsPDCQkJoX79+mRkZNCqVStje3FxsfFz8+bN8ff3Z926dbfUd1BQEPHx8dSpU6fc0siK8hs2bIiXlxeZmZkAhIeHEx4ejsViAWDXrl3k5uaye/duMjIySE1NBUpmtsLCwnjrrbdwdXU1+i5tv1ZFffzqV78qN664uDhSU1MZPXo0Fy9eJCEhgaCgIJ544gn27t1LcnIyL7zwAgBOTk5cvXoVZ2dnoqOjSUtLK7McU0REREREbp9m2v6Xk5MT48ePJyYmhjFjxrBixQpjueLGjRvLLWl8/vnnb/oAj1I+Pj4cPXqUb775Bh8fn5vmA/Ts2ZPY2Fh++uknoGS545kzZ8jNzSUnJwer1UpMTAyzZs0qt7xxypQprF+/3ij6rncrfQDYbDaSk5NZt24dMTExREVFGfv17t2bxMREsrOzadasGVByr9v7779vHJ+cnIyDg8MtXSMREREREamYZtqu0adPH9asWYObmxsDBgzAYrHg4OBA69atef3118vsW7duXcaOHcuGDRuAkoeJlM5mde3alcDAQGNfBwcH42mT1y+NrCgfoH79+vzhD39g0qRJFBcXU1RUZDzB0c/PzzjG39+fd955h9atWxt9u7i4EBERwZIlS3jkkUdYvnw5W7ZsMY4ZNGhQhX1MnTq1zPiSk5Pp2LGjUXh16NCB7777jpycHDw8PLh69SpdunQp0+9f/vIXhg4dSu3atfH09GTatGm3cOVFRERERKQyDrbSJ2SImOzy4WRT81x9vQHITThgaq57oD953xwzNdPl4fYAnPQPMjW3zYE48o4eNzXTpUM7AHJ2xd9kz6rl8WQvu53r1ZOnTc11bvMQhVnnTc2s49UQgJycHFNzPTw87Pbn1R7nao/PEsClA5+Zmuvm/xg/79xz8x2r2D3Bve3y/xyzP0tQ8nnK3XfQ1Ez3AD9yP/2XqZkA7o93t8tnGDD9c3xPcG9T86RymmkTALKysnj55ZfLtD388MNMnz7dTiMSERERERFQ0Sb/y8vLq8yXg4uIiIiISPWgB5GIiIiIiIhUYyraREREREREqjEVbSIiIiIiItWYijYREREREZFqTEWbiIiIiIhINabvaRMREREREanG9Mh/sZuCM+mm5jk2awrAhTUbTc2tN3QQaROnmZrZfHEkAJcOHjI1182vKwVpZ0zNdGzeDICMue+amtv4tZe5lJhkaqZbty4AZEX9xdRcrxeft9vv1R5f/myvP6/Zl/NMza3v6mKXzxLAycdDTM1t8+kOzr0ZaWomQJM3pnFxfaypmZ7hYXb7cu30V2ebmtl03kxydsWbmgng8WQvTvoHmZrZ5kAcAJkLokzNbTTlRVPzpHJaHikiIiIiIlKNqWgTERERERGpxlS0iYiIiIiIVGMq2kRERERERKoxFW0iIiIiIiLVmIo2ERERERGRakxFm4iIiIiISDWmok1ERERERKQaU9EmIiIiIiJSjalouwWxsbEEBwdjsViwWCz87W9/IzY2tsw++fn5zJ49G4vFwpAhQ9ixY4exbdeuXQwbNoxhw4bxxhtvkJ+fD4Cvr6/RZ//+/fniiy8A+Oc//8lzzz3HqFGjiIqKMsbg6+tLYWGh0W9ISEiZcTzxxBPs3bvXGE9YWBiZmZkAbN++nXfffbfC87NYLLz55pvG+5SUFNq1a3fD87r2mgwaNMgYx/XXasKECbd7uUVERERE5Bp17D2AmiIiIoKwsDCAcgUbwPvvv0+nTp2YOXMm+fn5TJgwgbZt21KnTh02bdrEBx98gJOTE1u2bCE6OpqXX36ZDh06YLVaAUhLS+PPf/4zPj4+rFy5kpUrV1KrVi1ee+01kpOTAfD09OTLL7+kS5cu/PDDD1y+fNnIT0lJ4bHHHmPbtm307NkTJycnJk+eTGRkJK+//jpWq5UPP/yw0vP79ttvjZ8TEhJo0qTJDc/r2mtSVFTEhAkTeOyxx8pdKxERERER+e9opq2KfP7550ah4uTkxJAhQ9i+fTvbt29n5MiRODk5ATBw4EAOHDhQ7viMjAwcHR0BuHz5Mh9//DE5OTnMnj2bzp07AxAYGMi+ffsAiI+Pp2fPnsbxO3fuJCwsjIyMDKOY69GjBwUFBUyYMIGJEyfi7Oxc6fhbt27N6dOnAfjPf/5Dy5Ytb3he16pduzahoaEkJSXd5lUTEREREZGbUdF2i5YvX24s+bPZbDfdv2XLlmRmZpKenm7MWpVycXEB4OjRo1gsFnr16sXy5ct56aWXAFi0aBEHDx4kKCiIqVOnkpeXB0DHjh05duwYAKmpqXTq1Mno88iRI3Tu3Bl/f3/i4+ON9tDQUL7//nt8fHxuON7SgvDcuXM0bdr0pud1PU9PT6P92mu1ePHiG+aKiIiIiMiNaXnkLbrZ8shr7zUDOHfuHF5eXhQXF5ORkUGrVq2MbcXFxQDG8sjk5GQiIyNp0KABubm55OXl8c4771BYWMif/vQntm/fjoODAwDNmjXjxIkTuLm5GW2pqamkp6czZswY8vLy8PDwoF+/fuTn5xMTE0P//v1ZsWIF48aNq/T8unTpwurVq3F1dSUwMJCUlJQbntf1srKyaNCgQblrJSIiIiIi/x3NtFURb29vNm/eDJQ8vGPFihUEBwcTEhLCihUrjIePbNy4EV9f33LHPvroo6xZs4b8/HxeffVVrly5Qp06dXjwwQeNIg8gICCAyMhI/P39jba4uDhmz55NTEwMq1ev5vz581y8eJElS5YwcOBAJk+eTFxcHGlpaZWO38nJCRcXF/71r3/h7e190/O6VlFREVu3bi0zJhERERERqRoq2u5QdHQ0YWFhhIWFkZSUxKRJkzhy5AiDBw9m+PDhDBw4kLZt29KuXTsGDBiAxWJh8ODBpKSk8OKLL5brb9y4caxduxYnJyfGjBnDkCFDsFgsHDt2jAEDBhj7de/ena+++go/Pz+j7cCBA3Tr1s1437NnT/7xj3/w+eefEx4ejpOTE1OmTOHtt9++4Tn5+fnh4uJC7dq1jbbKzgv+bxnksGHD6NevH40bNy7TXvoqXd4pIiIiIiK3T8sjb8H1S/1Ki7XrzZkzp8LjQ0NDCQ0NLdde+uRIgHvuucd4nH5wcHC52axr8z7//PMybdePZfz48QA899xzRpufn1+ZQq+icTzzzDM888wzZdqcnZ0rPK/KrkFl7SIiIiIicmdUtN1FsrKyePnll8u0Pfzww0yfPt1OIxIRERERkZtR0XYX8fLyKjO7JyIiIiIi1Z/uaRMREREREanGVLSJiIiIiIhUYyraREREREREqjEVbSIiIiIiItWYijYREREREZFqzMFms9nsPQgRERERERGpmB75L3ZTcC7D1DzHJo0BuLg+1tRcz/AwLh9ONjXT1dcbgEsHPjM1183/MdJff9vUzKZzZgCQ/upsc3PnzeT80hhTMxu+MBqAy0lfmJrr2sWHnJwcUzM9PDwAKEg7Y2quY/NmXPkyxdTMuo8+AmCXa3zps8OmZro95gtAbvx+U3Pde/Xg7LSZpmYC3Bc5mwtrN5uaWW/Ib8m+nGdqJkB9Vxeyov5iaqbXi89z8vEQUzMB2ny6g5w9+0zN9OgdAEDG3HdNzW382ss330lMoeWRIiIiIiIi1ZiKNhERERERkWpMRZuIiIiIiEg1pqJNRERERESkGlPRJiIiIiIiUo2paBMREREREanGVLSJiIiIiIhUYyraREREREREqjEVbSIiIiIiItWYijYREREREZFqrI69B/BLi42NZfny5Xh5eXHp0iVefvll/P39SUxMZNq0aezdu5fatWsDsGjRIr766iuKi4sZPnw4ffr0Yfr06Xz77bc4OzsD0LVrVwYMGEDv3r3ZtGkTnTp1AmDmzJlcvXqVCRMmEB4eTqtWrYwxREZGsnjxYh588EGef/55AKZP/3/snXdYVNfWxt+hiAVURAFBYjAx4tUYNWokakA0SjFYYouAWBI1GisGy1WJiooKiBWTCBhLKAoIqKCxgD2KJnqxIhqlKVWQIm3O9wffnDuDjObeyzp7YvbveXye4xk978ycOXvvtfda714MNzc3+Pj4oKKiApmZmejQoQPs7OwwefJkpKen47PPPkNiYiJatmyJjIwM8bqCIKCsrAx+fn7Q1dWtV8/MzOyla9TH1q1bcezYMRw+fBgAUFVVhb59+2LHjh346KOPsH//fhw9ehQ1NTXo27cv5s6di8zMTFGzqqoKXbp0wfLly1XeY933wuFwOBwOh8PhcP473vigDQCmTZuGUaNGoaSkBLNmzUL//v2RkJAAW1tbXLx4Ef3798cff/yB3NxcBAcHo7q6GmPHjoWNjQ0AwN/fH+3atROvl5GRAQsLCyQlJYlB2927d/H2228DAAYMGAAfH5+X3kdYWBhGjBgBExMTAECLFi2wd+9eZGRkYNu2bSr/Jz4+Hs7Ozjh27BjGjRv30nV//fVXREdHY+zYsWr16rtGfbx48QKZmZkwNzdHcnIy9PX1RY2UlBTs3bsXWlpa2LZtGyIiItCvXz8Vze3bt+Po0aPo1q2b2vfC4XA4HA6Hw+Fw/jv+VumRL168gIGBAeRyOdLS0jBx4kRxhalJkya4fv06zp8/D0EQEBYWBl1dXbXX6tGjB37//XcAwJ07d2BlZfVa/alTp8LPz+9Pvdfz589jzpw5OHbsWL2vP3369JXv789cQ4GtrS2SkpIAAElJSWKwGhcXhxkzZkBLq/ZnMmnSJPH7UmbMmDE4d+7caz8Th8PhcDgcDofD+c/5WwRtP/zwA9zc3DBixAgMHToUycnJ6NGjB959913cv38fFRUVMDExwT//+U8EBwfjk08+wY8//ij+/wULFsDNzQ1ubm745ZdfAADa2towNDREQUEBTp8+jYEDB4r//uzZs+K///rrr8Xztra2KCwsFIM9dWRmZqJ169Zo3bo1tLW18fTpU/G6rq6uGDBgAM6dO4dJkyap1VN3jfro168fLl26BADIy8tDmzZtAADZ2dlo27at+O/09fVRU1Pz0v9v2bIlcnJyXvnZORwOh8PhcDgczn/H3yo9sqKiAm5ubnjvvfeQMyAsrgAAIABJREFUmpqKqVOn4tmzZ0hMTMQ//vEPvPPOOwgKCkJxcTGmTZsGZ2dnAPWnRwLAJ598gjNnziAtLQ2fffaZ+PqrUgQXL16MZcuW4a233lL7fo8dO4b79+9j6tSpePr0KY4cOYIhQ4aI1z127BhiY2NhYGCAoqKievXqu8aUKVPq1WvatClqampw+/ZtdOjQQQzMWrVqhadPn8LCwgJA7Uplfat7ubm5MDIyeu1n53A4HA6Hw+FwOP85f4uVNgV6enpo0qQJUlJSEBYWhqCgIGzduhWHDx/G3bt3sW3bNgBA8+bNYWpqCrlc/srrffLJJzh8+DBat279p9/DO++8g/fffx+JiYlq/82pU6ewb98+BAUFYe/evYiPj1d5fejQodDR0cHJkyf/62vU5aOPPoKvry/s7OzEc8OGDcOOHTvE7yEwMBD29vYv/d/IyEgxpZLD4XA4HA6Hw+E0LH+LlbYffvgB0dHRkMvlsLa2xpMnTyCTyQAAnTt3xsOHD/HRRx/hxo0bGDlyJPT19TF48GC0b98eQG16pMI90tLSEtOmTQNQayRSXl7+UsCiSBFUsGDBApXXZ8+ejbi4uHrf69OnT6GnpwcDAwMAgKGhIZo2bYrKykqVf+fh4YG5c+diy5YtL+nNmzev3ms8ePBAxdlRmYEDByI4OBhWVlZiCqiNjQ3++OMPjB8/HkBtYDdu3DhkZWWJmjU1NejSpQscHByQmZlZ72fv0aNHvZocDofD4XA4HA7n9cgEQRBYvwnO35OqJ+rr7CjQNa117XwWHiWpbstxo1CW/Jukmk171QbKpecuSarbrH9fZC9fI6lm29X/BABkL1kpre46L+TtCJJUs/XMqQCAsstXJdVt2udDPH/+XFJNxaRTVUampLq67cxRfu26pJpNen4AAEy+49JLyZJqNuvbCwBQcuqMpLr6dp8ga5GXpJoAYLZ+JQpDIyXVNPzicxSUvZBUEwBaNW2M3K3fS6rZZvZ0pA5wkFQTADqejcfzk0mSahoMql0geLrWX1Jdk6ULXv+POJLwt1hp49Qyf/585OXliX/X0tLCTz/9xPAdcTgcDofD4XA4nNfBg7a/EZs2bWL9FjgcDofD4XA4HM5/yN/KiITD4XA4HA6Hw+Fw/mrwoI3D4XA4HA6Hw+FwNBgetHE4HA6Hw+FwOByOBsODNg6Hw+FwOBwOh8PRYLjlP4fD4XA4HA6Hw+FoMHyljcPhcDgcDofD4XA0GB60cTgcDofD4XA4HI4Gw4M2DofD4XA4HA6Hw9FgeNDG4XA4HA6Hw+FwOBoMD9o4HA6Hw+FwOBwOR4PhQRuHw+FwOBwOh8PhaDA8aONwOBxOg5Gamlrv+dOnT0v8TjgcDofDeXPg+7RxOBpEcHAwpkyZAgC4ePEirK2tAQCrVq3CihUryHSvXLmi9rXevXu/MZpubm6QyWQq5wRBgEwmw549e0g0WXHo0CG1r40YMYJMd+LEieJ3OXnyZISEhLx0/k2B1XecmpqKjh07vnT+9OnTGDhwIIkmq7aJQ09sbCycnZ0BqP62duzYgZkzZ7J8aw0Oi35HHU+ePIGpqSnZ9Vm1Txw6dFi/AQ7nddjZ2UEmk6Hu/IJMJsPJkyfJdLdt26b2tW+++YZEMzExURwYBQYGigOj+/fvk+gpiIqKUvsaVUfGQtPHx4fkuq+DxW84IyOD5LqvQ/kzVldX13u+oWHxrALsvuPVq1fXGxiHhISQBW2s2iZWEy1LlixR+9q6detINNevX49FixYBUA2k5syZgy1btpBoAsDBgwdFLeXf1qVLl8iCNlb3lUW/o/yMKt9jT09P0s/Kqn3i0MGDNo7Gc+rUKSa65ubmkmsqD2zVHVNANQjRNE1zc3Pk5+cjMTERBQUFMDU1hY2NDZo3b06qy+I3rAhWKisr8fz5cxgZGUmiqzwQU3fc0LB4VgF23zGLwJhV28RqomXkyJGSa968eVM8Vg6kCgsLSXVZ3FtW95VFv6P8jCrfY+pnh1X7xKGDB22cvwSnTp3CsWPHxIG2k5MT+vbtS6qp6LTT09NRWFgIExMTmJiYkGqyGPACQEVFBfz9/ZGYmIjq6mro6urCwcEBM2fOhK6u7hujee7cOfj4+MDR0RFGRka4f/8+AgMDsXLlSvLUmDt37uD48ePib9jBwQHt27cn0yssLMTy5cvx4MEDNG/eHHl5eejZsydWrFgBfX19Mt3KykpkZ2dDLpe/dEwFi2cVYPcds2gnWLVNrCZa+vTpg8rKSly7dk3U7d69O7S06KwA1AVM1N8xq4kWFveVRb+jjJT3lVX7xKGDB20cjWf37t24evUq3N3dYWRkhOzsbAQHByMtLQ0uLi5kuo8fP8aCBQugr68PIyMjZGVlQVtbG76+vmR56BkZGWKql/JxZmYmiZ6CNWvWoEOHDjh8+DB0dXVRWVmJ4OBgrF27Fl5eXm+MZmBgIPbu3QtDQ0PxnLu7O+bPn4+ffvqJRBMA4uLisH//fowfPx49e/ZEdnY25s+fj2nTpsHe3p5Ec/Xq1Rg+fDg+/fRT8VxsbCy8vLzg5+dHogkAurq68PT0rPeYChbPKsDuO2YRGLNqm1hNtKSkpGDRokXo3r07jIyMcOrUKXh5ecHf37/eesKGQMpgWJnc3FwcOnQIgiCoHOfl5ZFpsrqvLPodVveVVfvEoYMbkXA0njFjxiA8PFxlhrOqqgqTJk3C/v37yXRnzJiBmTNnolu3buK55ORkBAUFITAwkEQzOjpa7WuU6Trjxo1DeHj4S+fd3Nywd+/eN0bT1dUV+/bte+n8pEmTsHv3bhJNABg/fjx2796Nxo0bi+fKysowffp0ss86fvx4hIWFvXReakMQRZ0KJSyeVYDdd6yoB6qvRpJKl1Xb5OLigm3btqlMtBQUFJBPtEyePBmrVq2ChYWFeO7hw4fw9vZGUFAQiWb//v0xYMAAAMDZs2dVjs+dO0eiCbCpCWV1X1n0O926dUObNm3EQFhxnJ+fj+vXr5NoAprTB3AaDr7SxtF4Gjdu/FJKiq6uLnR0aH++xcXFKoNAAOjVq9crO7j/lZEjR0qekgMAenp69Z6n1GWhqe7acrmcTBOo/b0qB2wA0LRpU9LP2qhRI7Jrv4qcnBx4enpiy5YtaN68OY4ePYqIiAj4+/uT1VSweFYBdt+xusHlo0ePyDRZtU0ymUxlYA8ArVq1Ip8IqKysVAnYAMDS0hI1NTVkmv7+/uKxciBMXV9HadajDlb3lUW/c+PGDbJrvwpW7ROHDh60cTQedY049SKxuqCQcoDPIiUH+He6Vd2aioqKijdK88GDB/W6wj148IBME2DzGy4uLn7J3loQBDx//pxMEwBWrFiBCRMmiLUpTk5OkMlkWLZsGdmqF4tnFWD3HavDw8MDBw8eJLk2q7aJ1USLtra25Lp9+vRhUuelcLdVpqioCKWlpbh9+zaJJqv7yqLfAdjU5Wta+8T53+FBG0fj+e233zBo0CAA/061UqQWUKLI7VeGOs/fz88PO3fulDQlB6hdCVq0aNFLQQRlHRILTeWZbGWoZ7Jv376NiRMnqpwTBAF3794l0+zcuXO99tZWVlZkmgBQUlKCIUOGqJxzdHQkTWVm8awC7L5jdVBOArBqm1hNtCjX7SlDWcPHqs5L2d1WEASEhIQgPDwcmzdvJtNkdV9Z9Dus6vI1rX3i/O/woI2j8fzrX/9iouvk5FTvPieOjo5kmixScgD16VZVVVVvlGZNTY24v1RhYaGYnnPgwAH06dOHTPdVm5xSoc7a+smTJ6S66gIHyt8wi2cVYPcdq4MytYxV28RqomX27Nn1nqdMJWRllKTg0aNHWLp0KSwtLREZGUnqMMjqvrLod44cOaJSl29paYnevXtj0qRJpEGbprVPnAZA4HA0nKCgIPH4woUL4vHKlStJdQsKCuo9f+PGDTJNNze3/+h8Q7Fw4ULxWPn7ptRloal8bXXHFMTExIjH9+7dE4+3b99Opjlp0iTx2MfHRzym/qxbt24VNm/eLJSXlwuCIAhlZWXC1q1bVd5DQ8PiWRUEdt+xq6ur4ObmpvLH1dVV6NmzJ5kmq7ZJuc1Xvs8RERGkusrPqTKnTp0i03Rxcan3vLu7O5mmgpCQEGHIkCHCmTNnyLUEgd19ZdHvuLq61nt+4sSJZJqCwK594tBBW0HM4TQAiYmJ4rFyTUxqaiqp7ty5c8Xjb7/9VjzeuHEjmaYiJafuH2pbbeWZN+XvWyBMt2KhKQhsNghWrjNavXq1eHzp0iUyTVYbus6cORM6OjoYNmwYbGxsMGLECGhpacHDw4NMk8WzCrD7jn18fLBu3TqVPz4+PoiNjSXTZNU2Kbf5yvc5Li6OVFf5OZ08ebJ4HBISQqbJqs7LxcUFP/74I1xcXJCfn49Dhw6Jf6hgdV9Z9Dus6vJZtU8cOnh6JEfjkXJwrU5XuaGnfA8sUnLqovz5pNpTRipNVhsEswoW69Oh/qxaWlqYOXMmZs6cSaqjDItn9VXvQYoNp2NiYuDk5AQdHR1cv34dDx8+xIgRI8g0WbVNrJ4d5esrD34pdVnVeVlbW8Pa2hrFxcUoLi4m1VLAuk2sq0X5zP7+++8YNGjQS5+Nui5fGRb9Oqfh4UEbR+NhNdBWd31KXUU+/++//46srCy0bdsWPXr0INNTwGLzTxaaxcXFSE5Ohlwux/Pnz1WOKWHxG2a1oWt9g04F6mos/ldYPKt1ry/ld7x9+3bcvn0bQ4YMgY6ODlq1aoXvv/8eOTk5mDZtGommprVNrO4tpS6rOi9F4J2Tk4Ps7GyYmprCxMSEVFPT7islrCz/WbVPHDp40MbReJRdvJSPqdNylJGqwcvLy8OsWbPQrl07mJmZ4dixY/D29kZgYCCMjY3JdBXuhoIg4M6dO+IxpbshC00rKytERkbWe0yJwt1QEASVY0p3Q4XrqkJHcUw9u0s9wHwdUg5OWH3HiYmJiIiIED+rhYUFNm/ejPHjx5MFbazaJlYTLQpreLlc/tIxFX369EF5eTlOnTqFzMxMmJmZYeDAgWjWrBmZJgCUlZXB09MTeXl5MDMzw+PHj9G6dWv4+vqSmZGwuq8s+h0AuHXrFsLCwpCVlQUzMzOMGTMG77//Pqkmq/aJQ4dM4MmtHA0nOjpa7WuUA0TF3jV1HxGZTIaTJ0+SaHp4eGDs2LH46KOPxHMXLlxAREQEAgICSDSBVwfA5ubmb4wmK161yTOLjW2pKSkpQaNGjVQ2dz148CBGjx5NosfiWWWJm5tbvS54EydOxJ49e0g0WbVNixcvVhuIU63cArXfsTrUORD+r/zxxx+YM2cObG1txeApKSkJW7duRYcOHUg0AeC7775Dz5494ezsLJ6LiorCtWvX4O3tTaLJ6r6y6HeSkpIQGBiIWbNmwdzcHOnp6di+fTtmzJgBOzs7Ek3OmwlfaeNoPMqDBClR3rtGKnJycl76vB9//DG2bNlCqlt3A05lqDoyFpr1bSIr/P/ef5SDexaBGatA8ccff8TBgwchl8uxZs0atGvXDgsXLkTjxo3JgjYWzyrA7jtu2bIlUlNTVTa1Tk1NJd1rilXb5OPjQ3p9dVAFZq/Cx8cHfn5+KvfV2dkZ69evx/fff0+me+/ePXz33Xcq50aNGoWIiAgyTVb3lUW/s2vXLvzwww/iJukdOnTA+++/j9mzZ5MGbX+3ycK/Azxo42g8dnZ2MDc3FxtUxWy6TCYjm1UG2NTmqIN6QdzLywvGxsYYPHgweSoOS83PPvsMKSkp6Nq1KxwdHdGpUydJdK2srGBkZITGjRuL56iDxcjISOjo6MDR0RGWlpaSFfgfPXoUR44cwbNnzzB37lzk5eXh66+/JjXJYPWssvqOFy9ejNmzZ+Ptt9+GmZkZ0tPTkZ6eDj8/P0n0laH+zKwmWiZOnKj2Nap+p7S0VCVgA2rbDuqUQR2d+oeClKnGrO4ri35HJpOJAZuCVq1akeuyap84dPCgjaPxBAcHIz4+Hrm5uejfvz8cHBxgZGRErvvHH38gLy8Pjo6OsLa2VmvH3JB07NgRMTExGD58uHju0KFD5MHFhQsXcPLkSZw4cQI6OjoYOnQoBg4cqJLe9iZozp8/HwCQkpKCQ4cOIS0tDT179oSTk9NLGwc3JKtXr0ZiYiKaNWsGe3t7DBgwgHRVBABOnz6Nq1evIj4+HkePHkXfvn3h6OgIU1NTUl19fX3o6OigdevWyM3NxdatW8l/vyyeVYDdd2xubo4DBw7g6tWryM3NxcCBA9G9e3doa2uTabJqm1hNtBgbGyM1NRW2trYYOnQoWrRoQa6pblBNufEzAJiYmODXX39VWUm9ePEiqRkJq/vKot+Ry+WoqKiAnp6eeK68vJx8Y3pW7ROHDl7TxvnLUF1djXPnzuHYsWMoLS2FjY0NPv/8c1LNJ0+e4OjRo7h06RIsLCzg5OSEnj17kumVlpZi2bJlyMjIgIWFBbKysmBkZIQNGzZINitYXFyM48eP4/Tp02jWrBk2bNjwRmoCtcY2GzZsQFJSEq5fv06ul5+fj4SEBJw/fx6tW7eGo6Mj+vbtS64rl8vx66+/IjQ0FPn5+di/fz+ZlnJdlbraKwqkflbrIuV3XFNTg7Nnz6JFixaig2NBQQF8fHzInh3WbVNKSgqOHDki2UQLUGtGcvr0aRw/fhzV1dUYPHgwBg0ahKZNm5Lobd68GY0aNcLXX38tntuxYwfKy8tJ9znMz8/HnDlz0KZNG1hYWCAzMxMZGRkIDAwknyBlcV8VSNXvHD58GEeOHMHChQvF73fDhg0YOnQoaQaCMlK2Txw6eNDG+UtRUlKCEydOIC4uDqWlpQgLC5NM+/Hjx/D19UVKSgp5DU1BQQEyMjJgbGws6axYTU0Nzp8/j/j4eNy7dw/9+vXDggUL3ijNrKwsJCQk4OTJkzAwMIC9vT0GDx5M5pJWH1euXMGePXtw48YNJCUlkWoVFBQgISEBv/zyC2QyGYYOHYpx48aR6dnZ2WHUqFEAas0MFMeAdHUUUj6rgPTfsYeHB7S1tZGTk4OhQ4fC1NQUy5cvh6urK2bMmEGmC7BrmxRIPdGiID8/HytXrsTZs2fx22+/kWhUVVUhICAAv/zyCwwNDVFYWAhbW1t4enqqTWFsSFJSUpCeno42bdrgww8/lNSJVer7KnW/k5SUhNDQUKSnp8PY2BguLi4YPHgwmZ4yUrdPHDp40MbReBSBWkJCAoqLizF48GA4ODigbdu2kujfvHkTR48excWLF2FlZQVHR0f079+fRItV4fCZM2eQkJCA27dvw9raGo6OjujatSuZHivNsWPHoqSkBA4ODvj0009hYGAgDkzMzMxItZOTkxEfH49r166hZ8+ecHBwIB0YhYaG4vjx45DL5RgyZAjs7e0lSStm5fYKSPusAuy+4zFjxuDAgQOoqqrCyJEj0aRJE2zYsAGWlpZkmixNDVhNtFRUVCAxMRHx8fHIysqCjY2NWB9ETUFBAQwMDMjTqIHaNFd1UK4EsbivLPodVrBqnzh08KCNo/G8//77sLCwgIODA9q1a6cyyKXsUHx9fXH+/Hl06tQJDg4O6NevH/ls58CBA9UWDlMOeK2srNChQwd069btpSCCysiBhaayjbdCU1H8Tmlq079/fzRv3hwODg7o1auXyu+od+/eJJpWVlZo3769uIeW8ndM+VnLy8sRExMDfX19ODk5STJbz+JZBdh9x8opqJ999hl+/vlnGBgYkOkB7NomVhMt8+fPx6NHjzBw4EA4ODjg3XffJdNSwMpQRzkgj46OVrmfVAE5q/vKot9hZbrCqn3i0MGDNo7Gs3XrVrUDP8oZXisrK7Ru3VosUFbsA0Xd0CoKhx8/fixZ4fDly5fVvtanTx/k5OQ0+Aa6LDRfx/fff4/p06c3+HVZ7En0uv2Ibt26hX/84x8Nrjt79my0b98excXFMDQ0FM1fKGH1rLL6jpWDNsq92erCom1iNdGibMVeV5fqN/XFF1+oNdTp06cPiWZdpKpDZXVfWfQ7mzZtYmK6wqp94tDBgzbOX5558+aRbu6qjqSkJNjY2JBdX5MKh6UcGP7dNAHA29sby5Ytk1ST6rO6urpi3759EAQBkydPxu7duxtc4z+F+llVB9V33Lt3b3Tu3BmCIODOnTviMfWAV4EmtU1UEy2vIzIyksQIi7WhDqs2sC6s7ivl52dpulIfmnKvOX8ebvnP+cuTn5/PRDcoKIhsIFi3cNjZ2ZlE58/CYm7n76IJ1G5uKzVUn1VhO69Y7dIEKJ/VV0H1+V9Vg0SNprVN58+fZzK4j4mJIQnaTE1NMWXKFEyZMkU01Fm4cCGzDeRZweq+UrZZXbt2RdeuXUXTlcDAQEnNdOqiKe0z58/DgzbOXx4pHa6UoWjw6hYO+/r6akThMIvv+O+iyQqqz1pRUYHs7Gxxb6Ls7GzxWaE2e1EHq8EJ1XeclZWl9jVzc3MSTU1tm1jdW0rduoY6q1atItMC/l1zJQgC8vLyMGjQIEnSi1/Fm/jM1jVdWbt2LYnWn+Xv1N+9KfCgjcP5L6Fo8FauXCkWDickJODYsWPiazyNgfNXQFdXF4sWLYIgCNDV1YWnpycASJa6Vx9v2uAkKipK7WtUxjaa2jaxurcUunUNdebPny+JoY4mruK9Sc+ssunK8uXLRdOV4uJiSbea4fz14UEb5y/Pm7TE/7pZTVaFw3+XVMU36bf0Oqg+6+tMDFjVqrCA6juua16jWBWhRFPbpjeJXbt2oXXr1igqKsLly5clM9S5cuUKfH19oa+vDy8vL7z11ltkWpoOxTOrp6cHPT09XL58GVeuXBF1WE5kKd4D568FD9o4f3mkcmKqC0WD97rUJh8fH8ka+dLSUsTExGDChAkYPnw4uV5ZWRm0tLTQuHFjACDTTExMhK2tLUpKSrBr1y7o6OhgypQpaNq0KX766ScSTXUUFRWhRYsWMDQ0JNNIT09HYWEhTExMYGJiIp7/8ssvyTRfBYtaFVaDE6rvOCcnB56entiyZQuaN2+Oo0ePIiIiAv7+/mQpi5rUNinzJqVH3rlz55WvUxnq+Pn5Ye3atSgqKoKvry+2bNnS4Br/KVLeV+q+TlMmsh49egRtbW20a9cOALs+gPPfo/X6f8LhsKWoqAjr16+HXC5HWloavvjiC7i5uSEtLQ0AJHXdu3TpEubNmweg1sZdaqToyG7duoUVK1Zg6NChuHXrFgBg9OjRJDqff/45qqqqcPLkSQwZMgQjRowQU3UoNH19fREdHY2amhp4e3ujoKAA+vr6WLFiBQCoWGxT8ttvv8HT0xOOjo4AgM2bNze4xuPHjzF69GgsX74cP/30E+bNmwdXV1c8efIEAJgYcwA0v+GsrCy1fwC6Z9XOzg6DBg3CoEGDxONevXqhc+fOAOi+4xUrVmDChAlo3rw5AMDJyQnjxo2T3IFUGaq2aefOnS+de/bsmbjdi1QTLRUVFTh48CDGjBkDoLYtkZqgoCCS6zZq1AjvvPMOevbsiaKiIhKN+khMTAQAlJSUICAgANu2bUNZWRkAae6rVH3d6zh//jzJdS9cuAA7OztUVVUhIiICX331FRYsWICIiAgA7PoAzn8PX2njaDzfffcdevXqBQBYvXo1xo4di06dOmHt2rVknZgyRUVFiIqKwoEDB2BsbCxuPNqlSxdy7bpQmkfExcUhPDwcenp6KC4uxi+//IImTZqQ6AGAv78/1q1bB11dXQQEBOCHH37AW2+9hS+//FJlj6SG5NatWwgODkZ1dTXOnDmDxMRENGrUCF988QWJnjKlpaU4dOgQwsPD8fTpUyxdupTUYGDt2rVYsWIFunXrJp5LTk7GypUrERgYSKb7Oih+w4sWLVJxq5TJZLh//z5KSkpw48YNsmdVuRZIEASEhIQgPDycJAhXpqSkBEOGDFE55+joyNR6n6ptKi8vx9SpU7FhwwYYGRnh3Llz8PLyEvf5op5oSUtLQ2hoKOLj42Fra4tFixYBgMqqtVRIMWkn1QqXr68v0tPTMWDAAHh7e6NRo0bo0KEDVqxYAV9fX7L7yqKvex1U3/muXbsQHh4OXV1d/PjjjwgODoaJiQnc3NwwduxYEk0OLTxo42g8xcXFcHFxQWlpKVJTU8Wgqby8nFT3t99+Q2hoKFJSUmBvbw8jIyON2HOKAltbW4wYMQL+/v6wsLDAl19+Sd6JCYKA9957Dzk5OSgvLxfrYSgHYYqB5e+//4733ntP3Iy5srKSTBOoXRm5fv06Bg8ejO3bt2PlypXkKafFxcUqARsA9OrVC9u2bSPVZYFy+lF5eTl8fX1RVlZGtnF5XR49eoSlS5fC0tISkZGR5OYC6gZ5NTU1pLosmD9/Ps6cOYOpU6eic+fOuHv3Lnbu3ImOHTuS6h4+fBjh4eEAgBEjRiA1NRVr1qwh1XwdVIFxRkaG2C4oHwMQVzQbGlYTaCz6utdBdV9lMhnatGmD9PR0NGrUSNwTTqqMEk7Dw4M2zl+GixcviituQO3KBSVubm5wd3dHVFQUGjdujK+++opU789ANSM3e/ZsxMbG4t69exgxYoQkg7/q6mrI5XKcOnUK/fr1A1C7gqBIj6GgcePGOHToEI4ePQonJycAQGxsLJlNuoL79++jffv2sLS0hLGxsSTOaOpc5+RyObn2q6CcyU9OToaXlxeGDRuG8PBwcc84Snbv3o3Q0FAsW7YMAwYMINcDAGtra2zZsgXTpk1D48aNUV5ejqCgIPTo0UMS/fqgvK/m5ubQ0dFBSkoKrK2t8fbbb5NpKfDy8sK4cePg7u4OExMTJCQkkGuyYvbs2fUeK6AwumE1gcair2NFTU0NysrKEB8fL7ZNubm55N8xhw4etHE0HktLSyxduhTXr1/HihUrUFBQgICAAHzyySekujExMYiMjMTIkSPRq1cvFBcXk+pFCNE7AAAgAElEQVT9GagKhydMmIAJEyYgNTUVkZGRePDgAZYsWQJnZ2dYW1uTaDo5OeGzzz5DVVUVgoKCkJaWhm+//Za0OHrVqlXiRstjxozBpUuXcOTIEfj4+JBpAsDPP/+MtLQ0HDx4EFu3bkVFRQWuXLlCZs8O1HbOdTdhVuzDRI3UZi8VFRXw8/PD1atXsWnTJrz33nsNrlEfLi4u+OOPPzB9+nTk5+erfN8jRowg0505cya+//57DBs2DNXV1dDT08Pw4cPh4eFBpqlAamObffv2Yd++fVi1ahV69+6NnTt3Yty4cdi4cSPeeecdEk0AOH36NOLi4vDNN9/AwMAA+fn5ePHihWiUxAKqwFiRvaIOd3f3BjeZYTWBxqKvex1U99XNzQ3Dhw+HoaEhdu7ciVu3bmHu3Lnk+/5x6JAJ3POTo+HI5XKcPXsWJiYmsLKywr1793D27FlMnjxZkmX+mpoaJCUlITIyEmlpabC3txfNSBoaxSanwL9nN4uKilBaWorbt2+TaNZHdXU1Tp06hejoaNL6p5KSEjRp0gTa2trIycnB06dP8f7775PpHT9+/KVaIKmpqanB6dOnERkZidTUVJw4cYJE51VpkFQpT8C/a1X8/f3xz3/+U6xVSUlJITNvGDp0KEpKSjB69Gjo6uqqvEb5Wbdu3ap2BYJS93VQuNE9fvwYCxYsgL6+PoyMjJCVlQVtbW34+vrC1NS0QbWUmTdvHlatWiWargC1K6rLli2TbPXr7t27iIyMxIkTJ/Dhhx9i48aNkugCteZXYWFhCAgIwM2bN5nUUru5ub3WAfE/JT8/H0FBQTA3N4eLiwsuXbqEkJAQ+Pj4kLrp1kWKvm7nzp2YMWOGyrlnz55h2bJl2LZtG+RyOclYpry8XCX1s7i4GC9evICxsXGDa3EkQuBwNJyAgADWb0EkLy9PCAkJkURLLpcLQUFBwpAhQ4Rz586RasXExIjH9+7dE4+3bdsmueb27dvJNN3c3Miu/SoKCgrqPX/9+nWJ34kgHDlyhPT6kydPFgRBEKqqqgRra2uhoqJCEARBGD9+PJlmVFSU2j9/Ryh+59OnT3/p93rlyhVhxowZDa71OkpKSoTAwEDJdSsrK8mfH0EQhGfPngnBwcGCg4OD4O7uLhw6dIhc81VI2W7ev3+f9Pos+jp/f39hypQpQl5eniAIgnD27FnBzs6OfCzBqr/j0MGrETkaz9WrV5norl+/XjyOjY0FABgZGeHatWvk2o8ePYKrqysePHiAyMhIseaLioMHD4rHq1evFo9//fVXyTUvXbpEpllZWYns7Gy11vBUzJ07Vzz+9ttvxWMWtuHBwcGk12dRqzJy5EiMHDkSlpaW0NPTw9tvvy2eo0TZ8r+u9T9LBIIEGnXGNtSGUMooW7RnZGSQaik/p4pnRldXF2FhYWSaiq1AvvjiCzx//lw0v5Jin0ypOX/+PEaNGoWpU6ciKysL1dXV2LRpE6ZOnUqqy6Kvmz9/Ptzd3TF16lQsWbIE/v7+2LlzJyZNmkSmyXkz4TVtHI2nvtocBZR1Izdv3hSPDx48CGdnZwC1aQ2UsDA2UB7kqTt+EzTT0tKwaNGilzRkMhnpxsDKeop90uqelwpqTRa1Knl5eZg1axbatWsHMzMzHDt2DN7e3ggMDCRNBVK2/NckKIxuWBnbsLJoV35OExMTMWXKFAC0z48mml8BNJ9ZsYl3RkYGNm7ciJycHLz99tuIiYlpcC1lWPQ7ABszndu3b2PixIkq54T/L7ug7O84dPCgjaPxVFRUkM+q1geLxp2VsYHyIE/d8ZugaWVlxaSzUveZpHCRlFqThdnLunXrsGDBAnz00UfiuQsXLmDt2rUICAgg01U3mQTQPq8sYGVsowkW7cptPuXzw9L86sWLF9DW1n6pJhQASaaHvr4+LCwsYGFhgcWLF2P+/PmSPDMs+h1WZjodOnSQbNsTjjTwoI2j8ZibmzMp6mfRuPft2xfW1tYoLi6WtMNWDMgEQVA5phyQsdDUBKQK1BSmNvWtKlJiZGQET09PVFZWIj8/H3379kXfvn1JNXNyclQCNgD4+OOPsWXLFlJdLy8vGBsbY/DgwWjWrBmp1n8CxcSSk5NTvZNnjo6ODa6lDCuLdqnafGXeeecdeHp6wsPDA0lJSSgoKIC9vT2p+RVQu8/h7t27oaOjg+XLl6N///4qr9c10WgIlL9Tc3NzySY5WPQ7ycnJiIiIEM10vv76a/Tu3RuzZs0iNdNp1KgRuRsnR1p40MbReCwtLZnoKqyA6zumor49cqRAeUCmfEw5IGOhGRQURHbtV5GRkYFBgwaJg2nFMeVgkFXqXkFBAVasWIEHDx6gefPmyMvLQ8+ePbFixQryTafrQp3ydOHCBZw8eRInTpyAjo4Ohg4dioEDB4p1fFLz7bffYuPGjSTbKqibODt69GiDaynDyqJdkVomCALu3LkjHt+9e5dMU4G2tjbs7OxgZ2eH/Px8xMXFkeodPnwY8fHxKC0txcKFC18K2igoLi5GcnIy5HI5ysvLceXKFfE1yq1QWPQ79a32d+7cmTxQdXd3J70+R3q45T9H47l8+bLawS1l43758mW1r/Xp04dEU9nyX4FicH/y5EkSzVdx8eJF0oFRaWmpuEJRWFgIXV1d0oG9pmypoKCyspJsgM/K8n/BggVwcHDAp59+Kp6LjY1FUlIS/Pz8SDRXrVqFDz74QMWw4dChQ7h27ZpkexIVFxfj+PHjOH36NJo1a4YNGzZIoqvM6NGjVYwW3kTN6upqnD59GlFRUaTbkWRmZqp9jWr1Yv369Vi0aBGA2mdGUUc9Z84c0lXjiRMnimnjyseUKCZB64NFSh91X6fg1q1bCAsLw6lTp2Brawtvb28yLU37jjn/O3yljaPxREdHv3QuKSkJcrmc1GmwT58+SE1NhYGBgbgPUWVlJQIDA8mCNlarIzExMdi8eTOaNGkCf39/mJubw8vLC/fu3SOb5U1OToanpycOHTqE5s2bi85w/v7++OCDD0g0lb9fQRAQEhKC8PBwbN68mURPQXZ2Nvbs2YOWLVti0qRJ0NPTw4ULF7Bq1Sqy9BhWaTFZWVkqARsAODs7kw7sPTw8sGzZMuzbtw8WFhbIysqCkZGRZPtp1dTU4Pfff8fVq1fx5MkTcrdXTYJ63ldd3eDQoUNJdU1NTXH27Fm0aNECPXr0AFC7iuzj40MWkLMyv2KBImioqalBYWEhDA0Noa2tTa7Loq9jZaZT1z03MzMTGzduxMcff0yqy6GDB20cjUd5Rig3NxfLly9H9+7dyWfQ16xZg9u3b+P58+eYNWsWzM3NMX/+/JdqZxoSVsYGISEhiI2NRU5ODtauXYunT5/C3t6e1Dxi06ZN+Omnn8Q8/379+mHXrl1YtmwZ9u/fT6YL1G6psHTpUlhaWiIyMpI8bc/DwwOjRo1CZmYmNm/eDJlMhsTERNJZVmq7e3WwSA1s1qwZNm3ahIKCAmRkZMDY2Jh0w2cFZ86cQUJCAm7fvg1ra2u4uLiga9eu5Lr1tROCIDAZ3FPXe7GqG/T09IS2tjZycnIwdOhQmJqaYvny5XB1dSXTZOVsqC4VlNJlsKamBuvXr0dSUhIMDQ2Rn58PGxsbLFq0qF4zlIaCRV/HykxHeXI5LCwMwcHBWLly5UuTapy/Djxo4/xliI2NxbZt2/DNN9+IM5CUXLt2DZGRkSgpKYG7uztevHgBb29vslU2gN0AxcDAAPr6+tDX18eDBw+wZs0a8lQRLS0tWFhYqJyztLSElhbt9pEstlSQyWQYPXo0gNoUzX79+iE6Opo0wFFOBZXJZKipqYGZmRlWrVpF6lhWXFysUp8C1A46nz9/TqZZN4hRrjulnOyYNm0aOnTogG7duqGwsFBlsoEy/Uidmy7lZ2VlbMOqbvDx48c4cOAAqqqqMHLkSDRp0gR79+4lrbFmYX4FvHqykIotW7bAyMgIx44dA1C7dcSOHTuwadMmeHp6kumy6OtYmekAtVtXLF26FC1btkR4eDgMDQ0l0+Y0PDxo42g8+fn58PLyQnV1Nfbv3482bdpIoqsImvT19VFaWoqQkBC0bduWVJPVAKWuk5cUuf0AUFVVpTKrWllZiaqqKjI9VlsqKAeihoaGKpu6UlFfqu2NGzewatUqEqMKBZ07d0ZUVNRL562srMg0WWwJAoDZXkf11SQ+fvwY4eHhZJqsUrebNWsGZ2dnODs7i3WD8+fPJ68bVKyE6OrqQiaTITg4GAYGBmR6ABvzKwAvTbIoQ5VmffnyZYSGhop/19LSwjfffIOxY8eS6Clg0dexMtM5cOAAfvzxRyxcuBBDhgwh0+FIBw/aOBqPo6MjtLW1YWNjA39/f5XXpCqmNTY2Jg/YAHYDlLrWx1IEMxMmTMDXX38Nd3d3mJmZISMjA/v37yfttK2trZlsqaA8UGBpDd+tWzfyWV4WtSrvvfcek0FJnz59cO/ePbz33nuorq5GdHQ0dHR0VAxRKJHL5Th58iTCwsKQkZFBOvHAythGAcu6QUNDQ/KADYBK/6ac3kyd6swiw0NdCqS6TdwbChZ9nYKOHTti8eLFWLhwIU6fPo09e/aQBm0Kox4fHx+sX78eAFtjM87/Dg/aOBrP1q1bmeiqm/UE6INFqQco6myQKXFwcEDr1q0RFRWFnJwctG3bFl999RWpI6iLi0u96SH/+te/yDSBf1v+A7Wdpp2dHQBI3nkKgoCSkhJSDRa1Kvv27WMStO3evRvx8fHYv38/Nm7ciMePH6Ndu3a4efMmli1bRqb75MkThIeH4+jRo+jevTueP38upplRwcrYhlXdIIs6LxbmVwCbDA8DAwPcv38f7777rnju7t275MExi76OlZkOq9VxDh3c8p+j8Shb8Spm7oHapf8xY8aQ6aqz/JfJZGSBRd0BiqOjoyQDlPpQpFt9++23TPQpULazVuxpVfe8lKSmpqJjx44k1647UKisrMSpU6fQqVMnzJ8/n0QTqDWYadq0KaZPnw7g37UqZWVlZLUq48ePx6ZNm+o1bDAzMyPRBIDJkycjKCgIMpkMAwYMQEJCAvT19TF+/HiEhYWR6X7wwQdwdXXFrFmz0LRpU3z55ZfYtWsXmR5LrKysxLrBurVdlJNn6iz/5XL5S7W4DcWrzK+kSKkGpNu64vHjx5gzZw769esHCwsLZGZmIjExEdu2bUP79u1JNF/1Xij7ug8++EDtSiblKjWrLSQ4dPCVNo7GExgYKAZtc+fOFQfXcXFxpEFb3ZlNZdveAwcOkGiyMjZQIGW6VX170lHvmaY8qH/y5Em956mpqqrCsWPHEBoairy8PLIVkrozyHp6enB1dSXfOJdFrUpaWhoWLVpUr0kGZTCupaUFLS0tpKSkoF27dqILqVwuJ9MEak0coqKiMG7cODg5OaGiooJUD2BnbMOqbrDuyqJidTM6OhqJiYkkmizMr5SRMsPjrbfeQnh4OBITE5Geno53330XM2bMkCw9U8q+jlWturotJAoLC0l1OXTwoI2j8bCyQVaQlpaG0NBQxMfHw9bWVpy5ooDVAIVFuhWLPdPUubBRu7MBQHp6OkJDQ5GQkICamhr4+fmhV69eZHp1Z3Czs7NRXV1NpqeARa2KlZUVk2dHS0sLycnJOHDggJiemZycTJ7iZWNjAxsbGzx79gxxcXEoLS2Fm5sbnJ2dySayWBnbsK4bTEpKQmhoKJKTkzFlyhT8/PPPZFoszK8ANimoy5cvx+rVq8lTBOvCoq9jVauubrwkRX/HoYEHbRyNh5UN8uHDh0U3thEjRiA1NRVr1qwh1WQ1QBk6dChcXV0RHR0tpltJhdR7pimQsuOaNm0aqqurMWzYMMTFxWHu3LmkARsA/P7771i9ejXMzMzg4OCAnTt3Ql9fH5988glmzJhBpsuqVoUFS5cuRUBAAMzMzDBp0iRcvHgRa9euFQ0AqGnZsiXc3Nzg5uaGW7du1evaSYkUxjas6ga///57HD58GF26dMGkSZNQVVWFmTNnkunVRSrzK4BNhsejR49Irvs6WPZ1UteqSzle4kgDD9o4Gk9xcTGSk5Mhl8vx/PlzlWNKvLy8MG7cOLi7u8PExAQJCQmkegC7AQqLdCtA+j3TFIYgillHZUMQSvT19ZGdnY3c3Fw8f/5ckg7U29sb/v7+KCwsxLRp05CYmIgmTZrA1dWVNGhbvHgxZs+eXW+tChX11XOlp6cjLCyMtCbT0tJSZWXY2toacXFxKCgoINME1BsbSF3/KoWxjWKlSyaT4ciRIyp1g5SEh4fD1tYWn332GXr06IGgoCBSPYCd+RWLVeqMjAy1bQJlnReLvo6VmQ6rLSQ4dPCgjaPxWFlZITIyst5jSk6fPo24uDh88803MDAwQH5+Pl68eIHGjRuTabIaoLBIt2KxZxorNy1/f38UFRUhLi4Os2bNQkZGBqKjozFkyBCyGo4mTZqgffv2aN++PTp27IimTZsCoLfUtrCwUKlV6dixI3mtip6eHgBp61SAWofBwMBAtGzZEgsWLEDLli0RERGB7du3IykpiUyXxb506oxtbGxsSHVZ1Q2eOHECZ8+eRUhICJYvX46qqipkZmaSumiqs/ynnuSpqalRa/ZFVU+np6fHxJGURV/Hqlad1RYSHDp40MbReHx8fJjoNm/eHC4uLnBxccHdu3dx8OBBODo64sMPPxRdBxsaVgMUBVKmW7HYM0155rou1EYvLVq0gKurK1xdXXHnzh0cPHgQmzdvJjM1UB7oKe+TJkUtqFwuR1VVFeRyucrG4lSwqFMBautyPDw8kJmZCX9/fzx//hyFhYXkKxd1VyJevHgBPT090sE9K2MbVnWDWlpa4gC/oKAAsbGxolsnVV0bC/MrgI3ZV+vWrZkED+Xl5WjSpIlKX3fz5k1ER0eTabKqVecpkW8ePGjjaDz1uQxKvUFkp06d8M9//hOenp44ffo0mQ6rAQpQu8pnY2ODDRs2oLCwEDKZjDS1rL4UGIX1MhV//PEH8vLy4OjoCGtra0kCCgWVlZW4du0aCgoKYGpqiqVLl5Ka2vz+++9iKmheXp54nJ+fT6YJ1H7Hc+bMga2tLczMzHDr1i0EBgZiy5YtZC6DrOpUGjduLA52t27dinHjxklS93T//n3MmTMHYWFhaN68Oc6dOwd/f3/s2LEDb7/9NokmK2Mb1nWDANCqVStMmjQJkyZNUnHko0JK8yuAjdmXuhU86ome6dOnvxREdenSBV26dCHTZFWr7ubmBgsLC3z44YcvjaEo90Pl0MGDNo7G89lnnyElJQVdu3aFo6MjOnXqJImum5ub2pkqqo18WQ1Q/P39kZeXBxsbG1y9ehUeHh64evUqtm/fTlpLB0ib0hYaGoonT57g6NGjCA4OhoWFBZycnNCzZ08yTQBISUnBokWL0L17dxgZGeHUqVPw8vKCv78/2T5tN27ceOXriiC9ofHx8YGfn5/K53J2dsaGDRvw/fffN7gewK4mU3lwaWpqKplRhbe3N3x8fNC8eXMAwODBg9GqVSusXLkSISEhJJqsjG1Y1Q1mZGTAz88PmzZtwpAhQ1BTU4Pnz5+T/YYBNuZXABuzr9mzZ6v8XYotFVjBqlb92LFjOHLkCK5du4Z3330Xw4YNY7bvK6dh4EEbR+NRbASckpKCQ4cOIS0tDT179oSTkxPZJqfAy2mZ169fx7p16zBq1CgyTVYDlGvXrmHv3r0AgEaNGqFPnz7o3bs3Pv/8czJNViltpqammDJlCqZMmYLHjx/D19cXCxcuJK138/Pzw86dO1V+rw8fPoS3t7ckBgf1ERQURBK0lZaWvhSIWllZkRoHsahTAWpXT7OzsyGXyyGTyZCdnS2uTlBu6l1TU4Nu3bqpnOvZsycqKyvJNFkZ27CqG/T29sakSZMAACYmJti7dy/u3r2L9evXIzg4mESThfkVwM7sC5B2SwWg9vc0ceJElXOKzB2qNEZWtert27cXJ5Lu37+PI0eOYOvWrejSpQvmzJlDqs2hgQdtnL8MXbt2RdeuXZGRkYENGzYgMDAQ169fJ9NTFElXVVVhy5YtOHPmDHbs2IH333+fTJPVAAX496zqvHnzxL9TpmWytF6+efMmjh49iosXL8LKygqrVq0i1ausrHxpgsHS0pLcLv1VUKU+qbtuVVUViR7Apk4FqN2TztPT86Vj6k29BUEQB5rK5yjTFVkZ27CqGywtLUXfvn0B/Hv/tE6dOqGsrIxMk4X5FQB07txZcrMvVlsqdOjQgbx+uS6sa9WB2jZJW1sbRUVFzLZb4Pzv8KCN85cgKysLCQkJOHnyJAwMDGBvb4+1a9eS6968eRNLliyBjY0NDhw4gEaNGpHqsRqgyGQyFBUVoUWLFvjwww8BAM+ePSPVZJHS5uvri/Pnz6NTp05wcHDA/PnzyQedgKoRiDJSdtp1oUp96t27NwIDA/H111+L53bs2CEOgClgUacCQFydlhoHBwcsXboUX331FczNzZGRkYGQkBAMGjSITJOVsQ2rukHlSYadO3eKx+o2j28IWJhfAa83YfL29m7wND4WWyoAtZkkUrtWsqpVT0tLQ0JCAhITE2FmZgZHR0d8+eWX5JMAHDp40MbReMaMGYPS0lI4ODhg+fLlMDAwgEwmQ3FxMelGzJs2bUJ0dDQWL16M7t27Iy8vT3yNKvWJ1QBl6tSpmD59OmbPng0LCws8fvwYO3bswKxZs8g0WaS07dq1C61bt0ZRUREuX74MmUwmiamNuj2JMjMzyTRZMXPmTAQEBGDIkCEwNDREYWEhbG1txVWoN409e/bgwIEDKC0thZ6eHsaNGyem1VHh4uKCmJgYeHl5ITc3F2ZmZnB2diatB2VlbMOqbtDc3BwXL14U22MAuHTpEmnaqzJSmV/9Ge7du9fg12SxpQIAuLu7k16/PljVqjs5OaFDhw6wsbFBs2bNkJqaitTUVAC0e+Fx6JAJUvg/czj/A25ubuKxYraXOge9rq4ylLoTJ04Urz1u3DhSJ8W63Lx5ExEREcjMzISpqSnGjRtHmgqq7j1ER0eTFWeXlZWJaV1S8qo0PVZ75ri5uZGvFBUUFMDAwIB0dQKoXd3r3Lmzyjkp2ojdu3fj4cOH8PT0RLNmzVBSUoL169fDwsIC06ZNI9NVUF1djcLCQrRq1Urtaq5UUBnbjB8/Hps2bYJcLoeHhwc2bdokSd1gbm4uvvnmG7Rr1w4WFhZIT09HZmYmduzYgVatWpFovsr8ipVtPKDaL1FQWFiI2NhYMVWesq6N5bYvdSkoKCD7LQGa2e9w/jf4ShtH45k/fz65u199sEh9YmVsANSmk61cuZJUQ5mSkhLExMSgRYsWcHJygkwmQ+PGjXHnzh0yzRkzZjAZ/Hz00UeSawIQU14B4OLFi9DW1hatthcvXkyiWVFRgcjISEyYMAFr1qxBbm4uZDIZfHx80LZtWxJNFnUqQK07288//ywOtPX19eHl5YUJEyaQBm3V1dXYsGEDkpKSYGhoiPz8fNjY2GDRokXkAbI6qIxtWNUNtmnTBuHh4bh+/ToyMzMxYMAA9OzZk9RRkYX5FSuOHTuGQYMGQUdHB4aGhnB3d4e7uzv5lgp1g5XMzExs3LgRH3/8MZkmq1r1x48fY+7cuWTX50gPD9o4Gk9AQACzWcY9e/YgIiICZWVlkqQ+sRqg1LcXXlFREUpLS3H79m0SzXnz5uH999/Hv/71L2RkZKB58+bYuXMn6d5wrFi0aJGYignU3s/79++jpKTktdb8/y2HDx/G1q1bcfjwYezatQunT5+GsbExrly5glmzZpHVe61ZswZt2rQBULuX18aNG3HlyhUEBARg/fr1JJos6lSA2me07nOjo6NDHjht3boVRkZGotuqXC7Hjh07sGnTJmZpqFRJO6zqBg8dOqTy9/T0dKSnpwMAWRoqC/MrVly4cAGbNm2Cra0txowZI+7hSF2Hqrw/XFhYGIKDg7Fy5Up8+umnZJqsatWvXr1Ken2O9PCgjcNRgyL1KTw8XCX16YcffiCbRWc1QFG2uxcEASEhIQgPD1fZfqChKSkpwdy5cyEIAuzt7dG+fXvExMTA0NCQTJOF3TOgel/Ly8vh6+uLsrIy0tWhgwcP4tChQ9DV1UVYWBiioqLQqlUrjB8/nrRW8eHDh6Ibp7a2NszNzWFubo59+/aRaaqrU7l//z7effddMl1dXV08efIEpqam4rnMzEzyoO3y5csIDQ0V/66lpYVvvvkGY8eOJdV9FZQrUCzqBjMyMlT+Xl1djYiICBgYGJDWDkptfvVnoGiTV65cicrKSpw6dQobN25ESUkJRowYAUdHR/IU9idPnmDp0qVo2bIlwsPDSfscgF2tem5u7kuTDwoof8McOnjQxtF4FAXwykhhHsEq9YnFAEXBo0ePsHTpUlhaWiIyMpLU6EUxsJXJZGjSpAm2bdtGPkBhlUanIDk5GV5eXhg2bBjCw8NJ65C0tbXRpEkT3L9/H0ZGRjAyMgJAO7gGoLKNgfLGwArbdAoGDx4sHldVVeHYsWMIDQ1FXl4e6d5/CxYswIwZMzB27FhYWFggMzMToaGhWL16NZkmoN7BUAonVKlRTJ6FhYVJNnkGqBo13L17F0uWLIGjoyMWLlxIpsnC/EqBoiZxw4YNKCwshEwmw7fffgtDQ0OyybtGjRrB3t4e9vb2KCgowO7duzFw4ED8+uuvJHoAcODAAfz4449YuHCh6ORIDSsznYqKipcmHzh/bd68Fp7zxvHBBx8wWYFikfrEaoCi0A4NDcWyZcswYMAAUi1ANXho3ry5JDPKrNLoKioq4Ofnh6tXr2LTpk147733yDUFQUB2djYiIyPFWqOHDx+SB22NGzdGdnY22rZti7feegtA7ZYd1Pc3PT0doaGhSEhIQE1NDfz8/NCrVy9SzS5dumDXrqeBujsAACAASURBVF2IjY3FqVOnYGxsjJ07d5LV7ikwMDB4aRXx7t275Bbir4IqPZLV5BlQm3aq2E/su+++Q+/evUn1rl27hvbt2yM0NFRlJZU6E8Df3x95eXmwsbHB1atX4eHhgatXr2L79u1kplAKioqKkJCQgMOHD6NJkybkddUKx0YfHx8xXZt6EphVrbq5uTl3iXzD4EEbh6MGFqlPrAYoLi4u+OOPPzB9+nTk5+erpFRQpVH89ttv9VqIU3ae6tLoKisrSYMKZ2dnlJSUYPTo0Th+/DiOHz8uvkbVqc6ZMwezZ8+Gubk5fHx8cO3aNXh4eGDHjh0kesq606dPh6urq7h9RFhYGLy9vck0v/rqK9TU1GDYsGGIi4vD3LlzyQM2Ba1bt0aPHj1gYmICMzMz8oANqDWRmT17Nvr16yeu8CUmJta7rURDwsLYhlXd4P3797FkyRJ069YNkZGRkuxtxSo9/tq1a6J2o0aN0KdPH/Tu3Ruff/45mebRo0cRGxuLx48fw9nZGb6+vjAxMSHTU6BcCiAVrGrVLS0tya7NYQMP2jgaj7p0trS0NLF4mQIWqU+sBih9+/aFtbU1iouLUVxcTKql4Ndff5Xcfv8f//gH1q9fj5YtW2LSpEnQ09PD+fPnsXr1aiQkJJDpzpgxo97zlKte3bt3x8GDB8W/d+3alTRVUFn3xx9/RExMDG7evAlTU1Ps2LFDDGYUgXlDYmBggOzsbOTm5uL58+fkq4kK8vLyMGvWLLRr1w5mZmY4fvw4vL29ERgYCGNjYzJdCwsLhIeHIzExEenp6ejYsSNmzJhBmoLKytiGVd3gyJEj0axZMzRu3FicMJOi/lVq8ysFimdm3rx54t8pV24TExMxefJkyZ11169fj0WLFgEAYmNj4ezsDKB2smnLli0kmqyC8VWrViE1NRUGBgbi81NZWYnAwEDuKvlXReBwNJxz584JI0eOFKZMmSJkZmYKVVVVgr+/v2BjY0OunZubKwQFBQnfffedsH37diErK4tUb8qUKUJ2drbKuYyMDMHd3Z1UlwVubm6Sa37xxRfCgQMHhICAAGH9+vXChg0bBEdHR+HKlSuSv5cLFy4Ic+fOJbt+VlaW4OPjI+zcuVN48eKFIAiCcP78eWHo0KFkmn8Gqvv+7NkzYe/evcKoUaOEPn36CFFRUUJJSQmJloIFCxYIly5dUjl3/vx50vuq4O7du4IgCEJVVZUQEREhREVFCTU1NWR67u7uQllZmSAIgvDJJ58IeXl5glwuF8aOHUumKQiCkJKSIgwfPlzYv3+/cObMGSE0NFRwdnYWrl+/TqqbkZGh9g8VISEhwooVK8Tf7fPnz4Vly5YJ33//PZmmIAiCq6ur8OzZM5VzhYWFwsSJE8k0Y2JixON79+6Jx9u3byfTFATV9kf52NXVlVT3p59+EoYNGyYMHDhQsLe3F0JCQkj1BEEQvL29BRcXF8HZ2VlISEgQUlJShE8//VRYtmwZuTaHBr7SxtF4fH19sWXLFmRkZGDjxo3IycnB22+/jZiYGFLdrKwsAIC9vb14ThAEZGVlkeWhszI2qM/yX5DA7EVqZDIZRo8eDaD2M/fr1w/R0dGSObQVFRUhKioKBw4cgLGxMekGpx4eHhg1ahQyMzOxefNmyGQyJCYmkqYp/hkEotqnFi1awNXVFa6urrhz5w4OHjyIzZs3IzExkUQPAHJycl5aKfj444/JZuwV7N69G/Hx8di/fz82btyIx48fo127drh58yZZDRIrYxtWdYOK9r8+qOpiWaXHT506FdOnT8fs2bPFlOYdO3aQuswePHhQXOVavXq1uHp56dIlUqMO5fZH+Zjyd8yqVv3atWuIjIxESUkJ3N3d8eLFC3h7e6tse8D5a8GDNo7Go6+vDwsLC1hYWGDx4sWYP3++JHa1dnZ2ol25AoE4PYbVAIVFnj8L+31lFy9DQ0PyYFjBb7/9htDQUKSkpMDe3h5GRkbYvXs3qSbrAPVV76uhUR5gy2QyGBsbY9myZWIalNRQBaYKkpKSEBoaCplMhiNHjiAhIQH6+voYP348mabAyNgGYFM3GBUVpfY1KkMSVunxtra2MDY2Rnh4ODIzM9G2bVssWbKEdH84dcET9bOj/P1KlUbNKhhXpEvr6+ujtLQUISEhkjw7HDp40MbReJQbVnNzc8n2FwkODkZ8fDxyc3PRr18/ODo6irPLlLAYoDg7O+Pzzz+Hs7Mz+Z41CljY7yv/lijrf+ri5uYGd3d3REVFoXHjxvjqq6/INVkFqCyou3l5WVkZSkpKsG7dOvTs2ZNMt2PHjoiJicHw4cPFc4cOHUKnTp3INIHae6ulpYWUlBS0a9dO3JpDLpeTabIytmFVN+ji4oKuXbuSXb8+WNXvAbX1vsrOjenp6di4cSO+/fZbEj11wRN1IPXgwQMsWbKk3mMqWAXjyhgbG/OA7Q2AB20cjae4uBjJycmQy+UoLy/HlStXxNcoLZg//vhjfPzxx6iursa5c+fg6+uL0tJS2NjYkLlqsRqgKFb3pk6dirfeegujR49G//79yfQANvb7GRkZ4p5/giDAzs4OAMjTQGNiYhAZGYmRI0eiV69ekpi9sApQXwfFTHp9hf5ZWVlYuHAhfv755wbXU+Dh4YFly5Zh3759sLCwQFZWFoyMjLBhwwYyTaA2aEtOTsaBAwfEvaaSk5NJjSNYGdusW7cOCxYsUElDvXDhAtauXYuAgAAy3YCAABQUFGDkyJEYPnw4mjdvTqalgFV6vAK5XI6TJ08iLCwMGRkZpBOkio2fBUFQOVbem44Cf39/8Vg5PZ0yVZ1VMK4uQAXUG7xxNBuZQL0WzeH8jyg3NHWRquEpKSnBiRMnEBcXh9LSUoSFhZHoeHh4YOzYsS8NUCIiIkgHKMrcvHkT0dHRuHHjBj755BMyS/oTJ06obIrMktTUVHTs2JFcp6amBklJSYiMjERaWhrs7OxE++eGRrlOsW7tBmWAeu3atVeubu3cuVOtm2ZD4+rqin379pHrFBQUICMjA8bGxioDMyoePnyIgIAAmJubY+HChfj111+xdu1aBAYGol27diSa2dnZ2LNnj4rz6oULF7Bq1SpS51U3N7d6g/Lx48eTtcMKcnNzERsbi/j4eLRv3x6jR4+GtbX1/7V371FRVusfwL8jInJTBC+FomVaGJq3RE0Mj3U0UVET8JJ4KfKSoPYTBU3zKKahy0tihufkJbGDoIANpHASBS+o6PFWGh5RSxhviIiKiNx+f7iYMyhjnZo9+53h+1nLtd55p8XzOMk777PfvZ8tNOatW7egVquRk5ODJk2aYNiwYcKfkFy/fh0xMTHYtWsXOnXqhMuXLyM2NlZozGdtTyFybzHdQd8niRoEPnv2LD755JMai/HXXntNSEwAyMzMrPG8SqUSvucgicGijUzS/fv3oVarMXr0aKEx9uzZg+TkZNy9exdvv/02BgwYIPQLVOYNiq7Dhw8jKSkJ586dQ0JCgpAYsovx0tJSpKSkIDo6Grdu3TLKUwNd+fn58PDwwM8//2zUuKIL1LFjxwptif57PXr0CH5+ftX2HDSWcePG4ZtvvjF63Crr16/HpEmTDPozR48erW1sU1JSom1ss3DhQqF74um7Jo4YMQIxMTHC4j7p3LlziI+Px4EDB4RdK57V/ETkJswdO3bEmDFjMHXqVNjY2CAgIABff/21sHjPsmvXLnh5eQn7+a6urnBxcUHXrl2fmrIo8ntHtxhv2rQphg4davTpiiUlJUhMTERMTAy2b99u1NhkGJweSSbl3Llz2LZtG/bt26ddDC9Kz5494eLiggEDBqBFixZQqVQ4evQoAHEbTutjjLGVc+fOQa1W48CBA+jatStGjBghdBTwyekoGo0Gy5cvxxtvvCEsJvB4rUZ0dDSSk5NRXl6OFStWGG0jZl1OTk549dVXjRJLdoEq2pOj9qWlpTh48KDQzYGf5f79+1LiVjl06JDBizZZjW1krRusUlFRgYyMDKjValy+fBl+fn7CYslofgUAa9asQXx8PEaMGIGBAweipKREWKzfsnHjRqFFW0pKCr7//nucOHECbdq0waBBg4yydlHGWvUqFy9eRHR0NHbv3o0+ffpIa9BEfx6LNlI83dEhKysr3L17F//6179gbW0tNO7EiRO1I3EajUZorCqyblCqmqwMHz4cM2bMQP369YXGA1Ct7fC2bduwceNGLFy4EH/961+FxZw4cSLKysowaNAgJCYmYvr06VIKtiqiF93LKFBPnTqlXTdYRfT2EU+ujbSyssKQIUPQunVrIfF+i7G60ukjYpBHVmMbWesGz5w5A7VajfT0dPTo0QOjRo1C586dhcaU1fzK09MTnp6euHPnjnYJgL+/P7y9veHr6ys8vi7RA5StWrXSbimQnZ2N77//HhEREXBzc8O0adOExJS1Vj0pKUn7NHro0KG4cOECPvvsM2HxSDwWbaR4ffr0wdChQ7Fy5Uq4uLggICBAeMEGAEFBQcJjPEnWDUpkZCRatmwpNEZNrl+/jrlz58LBwQExMTHCO1fa2dnh2rVryMvLw71794x2c61vKqjIwQBZBWrHjh1rnM4mkr4mAps3b8b48eOFxdW3LufmzZvCYv4eIv5dy2psY2tri1WrVhl93WB4eDh8fHwwc+ZMo3zfAHKaX+lycHCAv78//P39tWubjc2YAx4qlQoWFhYoLCzEr7/+KiyOrGY6CxYswIgRIzBu3Dg0a9ZM6NpTMg4WbaR4QUFBUKvV+M9//oOhQ4eivLzcKHFdXV3h5ORU7amT6CcGsm5QPvnkE72ba4ualrN9+3b84x//QHBwsLYDnmgrV65EYWEhEhMTMXXqVOTm5iIhIQH9+vUTeiOqr6gQ2bFMVoGqJElJSUKLtoYNG2rb7etuOfDxxx8LiymLrM6rVRwdHeHo6Kh9LXrd4LJly6BSqVBQUICCgoJq74lcXwY8bgf/+uuva598bd++3ehTfd3c3IQOFuo2SqpS9Z0j0sWLF5GcnIy0tDQ4OzvDy8sLAQEBQmeX3Lx5s1rBBjwu0NesWSMsJgDs27cPiYmJCAwMhL29PfLz8/Hw4UOjzKQhMdiIhEzGhQsXEBcXh927d+ONN96At7e30E5e27dvR1paGmxtbfHOO++gd+/eRt1XRZfoG5RnPfER1Za/6qYPQLUuh8a6CQSArKws7NixA3v27EFaWppRYhpTVYGakJCA3NxchIaGCi9QNRqN0bdy0MfHx6dam3pD0226MmPGDKN1eP0t+pp3iGCszqtPGj58OOLi4oT9fH9//2qFeJXi4mJh/6ZkNL96FpG/P+Hh4dq1VbpLAoKCghARESEkJvB4MLZ169bw9PR86jooqmulEprpnD9/HnFxcdizZw+6du2K5cuXGyUuGRaftJHJaNu2LUJDQxEcHIy9e/diy5YtQos2X19f+Pr6Ij8/H8nJyZg+fToaN24MLy8v9OjRQ1jcmohubKDvJnvZsmXCWtL3799f75e2KMXFxVCr1bC1tcXAgQPh6uqKefPmme3C7IYNG2LMmDEYM2aMtkD94osvhBaoVTe7VapuekUW4zV13ausrERpaamQeLoxqty+fVtoLF2TJ09GZGSk3vdFd65UQmMb0U9k9BW9Ip94Kan5lWhnz57VHsfFxWmv/4WFhULjytifTGYznYcPH8LCwgKvvPIK5s6di1mzZmHfvn3C45IYLNpI8S5evIgvv/wSdnZ2mD59OpycnNCvXz+jTalzcnLCe++9h5dffhlbtmxBSEgI0tPTjRK7iqypbfr2eTEEGV/as2fPRqtWrXD37l1cuHBBO41N1hNUkZ58AlJVoIruzjl48GD89NNPaN++Pby8vIxyY6Kv6Ba9IbKs38sHDx48833dpiGGJKOxjdLWDYr8fy6j+RUg5zPWHfDQdyxC+/bta3wyLLKQkbVWPSoqCps3b0bdunUxf/58eHh4wNLS0mj3TmR4LNpI8RYsWICJEyfi7t27WLZsGcLDw40W+/jx49i9e7d2w+Bx48aha9euwuIp7QZFJBlf2gUFBYiIiEBlZSUmTJggLI4ShIWFaafuTZgwAZs2bQLwuDmH7tRUQ6sqhH/66Sfs3LkTFy9eRJcuXTBw4EC4uLgIiRkVFaUdUTZmAX7p0iVtkxndY0DsiP6TsXSJiiursU1tWjcoo/kVIOcz1i1+9R2LoO+6uGnTJvzlL38RElPWWvWkpCTs3r0bRUVFCA4OhoeHh/CYJBaLNlK8OnXq4M033wQAoetTnuTh4YEGDRpgwIABmD17NurWrYvKykocP34c3bp1ExJT1g3KsWPHnjpXWVmJoqIiYTFlfGlbWFhoY5j7cl7dv19ZWVmN50Vq37492rdvj9zcXCxbtgxfffUVTp8+LSTW1q1bsWnTJtStWxeffvopevXqJSTOk1auXKk9FtlU5kmNGzc2ajxAXmObH374Qcq6QRkdX2U0vwLkfMb6BjwuXbokNK7M66Kxm+lYWVmhXr16qFevnvCp4mQcLNrIpFRUVBgtloeHB1QqFa5evQq1Wl3tPVFFm6wblPj4+BrPd+rUSVhMGV/aJSUluHbtGioqKlBSUlJtLZTojnDGJmskG3i8xiw5ORmpqamwt7fHO++8gyVLlgiLl5iYWG1E2VhFm+5eg8Zkb29v9NiyOq/KWjcoo+NrWFiYlOZXMj5jfQMeogcjZF4XnyR6rTqZHxZtpHh5eXnYuXMnKisrtcdVRC7O/vzzzwEA5eXlKCgoQKNGjbRPakSRdYMiY3G2jC9tS0tLbWMVS0tLhIaGorKyEg8fPsT27duFxZVBt0B99OhRtWOR/Pz8cP/+fQwYMADz58+Hvb09VCoV7t69q32KbGi1bUTZy8tLSlwZjW1krRuUUZDLan4l4zOWNeDx5LXQWNfFmoj+3H/++WeMHTsWlZWVyMrK0h6L3MqHxGLRRoo3cOBA5ObmPnUsWnl5OcLDw5Geno5GjRohPz8fnp6eCAkJETb6KesGpaY9c6qImpYj40tbtyPcrVu3sGPHDmzfvl3KxuKi5efnIyQkBJWVlU8VqyJZWVnBysoKmZmZyMzM1E5FNcfCWJb8/Hy9619FtS2X1dhG1rpBmYzd/Ko2fcZPXguNcV2UtVZdd4BblzFnLJFhcZ82MklXrlxBTEwMZs2aJSzGqlWrYGNjg0mTJgF4fKFbt24dHjx4IKwNvoeHB3r37g0AOHDggPYYMO6XZ0pKClasWIGAgAD4+fkZLa4xHDlyBNHR0cjKygLweAG6uU2NBB5vqfDRRx9VazNtbE8WxlWL/g2tW7duaNeunXZEuerYXEeUExISqr2+d+8eIiMj0bp1a2zdulVITN096XQbOOieF+FZHWxlPa0R6cnmVwMGDEDXrl2FDujVts/Y2KKiompcqw4Ydy3s9evXERMTg4SEBLPcl7Q24JM2MhkVFRVITU3Ftm3bkJubK3zfmszMTERHR2tf16lTB4GBgUKLGFmNDaoUFhZi4cKFuHPnDjZv3mx2xcygQYPQoUMHjB49Gu7u7vjwww/N7u9YJSYmBkuWLEFqairCwsLQsGFDo8V+sjCOiooS+jnrG1E2V7rXhgMHDiAyMhITJ07EuHHjhMWU1cChNhUNMppfAbXrM9ZoNAgPD8eqVatw6tQpBAcHo379+liyZAk6d+4sJKastepV0tPTER0djePHj+P999/HP//5T6PGJ8Nh0UaKVzU6tGvXLnTq1An37t0zymau+qZL1K0r7tdG5pfnnj17sHz5ckyYMAEjR46UlodInp6eOHjwICwtLWFpaSltOqoxODg4YNmyZcjIyICfnx+6dOmifU/kU1sZhXFiYiImT55c7dydO3cwb948vVOTTF1RURGWLl2KS5cuYf369XjhhReExlNSAwdzJaP5VW2zePFijBgxAhYWFggPD8eSJUvQunVrBAcH691Q/c+StVZ9/fr1SEpKgpubG8aPH4/S0lJ89NFHRotPhseijRSvf//+GDNmDBISEmBjY4OAgACjxLW3t0d2djbatGmjPZeVlQV7e3ujxDem4OBgHD58GMHBwWjRokW1LQDM6WZh1qxZmDlzJtLT07Fp0yacPn0aa9euhbe3t1mua8vOzkZERATc3d0xePBgo8SUURgXFxfjgw8+wLJly+Dk5ISDBw9iwYIF8Pf3Fx5bhoyMDCxatAh+fn5YvHixUWLKamxTm8hoflXbPHr0CH369MGdO3dw48YN9OzZE8Djz1wUWQMbMTEx6NOnDwYPHozOnTtjw4YNUvIgw+GaNlK89PR0xMfH49KlSxg4cCAOHTokbERM15UrVzBt2jT06tULLi4u0Gg0SEtLw9q1a9GqVSvh8Y1J355EgPktRNd1+/ZtqNVqJCQk4LvvvpOdjkFFREQgKSnJqPuWVamoqND+3h49ehRjx44VXhjv378fK1euRLt27XD+/HmEh4dXa5xhTlxdXWFjYwMHBwftDaHo/bzefvttODs71zgd0hjX49pARvOr2qZqPeZ3332HjIwMhIeHo7S0FD4+PsK+A2StVa+oqMCBAwcQFxeHS5cuobS0FBs3bkTz5s2FxSSxWLSRybhz5w4SExORkJAAW1tbeHt7w9fXV2jMY8eOIS8vD1evXsXDhw/Rvn179OnTR2hMmTjCaz7mz5+PkJAQYW32fy9jFcYXL15ESEgISkpK0LNnT8yaNYs3ugakhMY25k5G86vaZt26dTh69Cg0Gg0iIiLQoEEDLFq0CD169MCECROExFRCo5eq6/DOnTthY2PDdW0mikUbmaRz584hPj4e8+bNExYjJiYGcXFx2LhxI+zs7JCVlYX58+dj1KhRePfdd4XFlYEjvGTKtm7diq1bt2LRokXo1q0bIiMj8cMPP2D58uV46aWXZKdncOXl5Vi/fj0CAgJQr149HDp0CKdOncKUKVNQp04dITHv3LmDJUuW4OHDh0ZvbFNbjBo1qlrzqyp+fn6IjY2VkJF5unjxIho1agRHR0dcuXIF586dwzvvvCM7LaM5e/Ys3NzcZKdBf4CYqzuRgVXtUbNs2TLMmTMHW7duxdSpU4XG3LFjR7VWva6urvjmm2/McoRqzZo1cHJyQkpKCrZt24aUlBQ4ODhg1apVslMj+k3Hjx9HbGws3N3doVKpMGXKFMydO1f4NUKWpUuXVmto0K5dO+Tn52vXRIlQ1dhm5MiR8PPzw5w5c7R/yDBkNL+qjV566SU4OjoCAJo1a4b79+8Ln7UjQ25uLj7++GMAQL9+/fDWW2/B3d2d61BNGIs2UryVK1dqu0X++9//xrBhw+Di4oIvv/xSaFxra2tYWVlVO2djYwMbGxuhcWXIzMzUTskB/ru9wfHjxyVmRfT7rF69Gg0aNKh27vXXXxe2L5xsP/74I+bNm4d69eoBABwdHTF//nycPHlSaFzdxjbDhg3T/iHDqGp+pctcm1/JdvHiRSxevBh9+/bFyZMnERISIjslg6vqlAk8Lk5TU1MRFRWFiIgIyZnRH8XhG1K8EydOaBe616tXD+7u7ujWrRuGDx8uNK6VlRVu3LiBZs2aac/duHFD+L5EMnCEl0yZ7mbP4eHh2huwkJAQs9xcu6pY06VSqVC/fn1hMWU2tqktQkJC9Da/IsNISkpCTEwMAGDo0KG4cOECPvvsM8lZiVFUVIQePXoAAGxtbQEAr7zyCh48eCAzLfoTeEdGJqGqQ9qMGTO0r0WPPs6cOROTJk1C79694ezsjJycHGRkZGDhwoVC48pQm7Y3IPOju9nz2bNntcfmOMACAM7Ozjh8+LC2XTnweENzkevMbt68ibi4OOmNbcxZy5Yt8cknn2ibX1lZWWHmzJlm161YpgULFmDEiBEYN24cmjVrhuTkZNkpCVNaWqo9joyM1B5znbrpYtFGiqdSqVBYWIiGDRuia9euAB4vihfN1dUVW7ZsQWpqKvLy8uDq6orJkyc/NQ3LHISGhiIoKIgjvGTydAs1c934ec6cOfi///s/rF69Wjug1LBhQyxbtkxYzLCwMGE/mx7T1/zq9u3bZtf8SpZ9+/YhMTERgYGBsLe3R35+Ph4+fCj0KbUszZs3r3Fwx9nZWWJW9GeweyQpXlpaGiIjIxEUFAQXFxdcuXIF69atw9SpUzlNx0COHTuG4uJiFBcXIzc3F02aNIG9vT3s7OzManNtMk9jx47VToPUd2yONBoN8vLy8Pzzz1ebxp2eng5PT0+JmdEf4evri61bt1ZbS/3gwQOMHTsWO3bskJiZeTp//jzi4uKQmpqKLl26YPny5bJTMqi8vDwEBgaiRYsWcHFxQU5ODjQaDdatW6dtxEKmhU/aSPH69OmDJk2aIDY2FhqNBs899xzmzJmDDh06yE7NbMTHx1d7nZ2djf3796O8vBxHjhyRlBXR73Py5Em89dZbqKysxK1bt7TH+fn5slMTqnnz5jVulLthwwYWbSaoNjW/kiU/Px8bNmzA888/j169euHgwYNQqVRmOQDcpEkTxMTE4PTp09BoNOjduze6dOlitjMQagMWbWQS3NzczHItmVIsXbpUe5yXl4f58+ejY8eOWLRokcSsiH6fH3/8UXYKisIJNKapNjW/kiU4OBheXl4oKCiAv78/vv32WzRq1AhTpkzB0KFDZadnUDt37qz2OicnBzk5OQBgdn/X2oJFGyle3759nxoZKiwsRFFREX7++WdJWZkntVqNtWvXIjAwEN7e3rLTIfpd9K29VKlUZrtX27NwJN001abmV7KUlpZq92Tbs2cPXnjhBQB46gmnOcjNza32uqysDLGxsbC3t2fRZqJYtJHi7d27V3tcWVmJTZs2ISYmBl988YXErMxLfn4+FixYgLKyMnz77bdo0qSJ7JSIfrcnpwjeu3cPkZGRaN26da0s2sg01abmV7LUqfPf7Yl1m4+Ul5fLf+eCYgAADntJREFUSEeowMBA7fH58+cxZ84ceHl5ITg4WGJW9GewEQmZjF9//RVz587Fiy++iNDQULaeNqDu3bvDwsKixnUwulMniZTuwIEDWLx4MUaNGoVx48bVyqdO/v7+2r0tiei/PDw80Lt3bwCPrxW6xwcPHpSZmhAVFRVYv349kpKS8Le//Y2NxUwcizYyCZs3b0Z0dDTmzZunvciS4WRmZup9z93d3YiZEP0xRUVFWLp0KS5duoQlS5Zopz2Zqzt37uDEiRMoKiqCvb09OnToACcnJwCP96pzc3OTnCGR8tSm77rs7GzMmTMHr732GmbNmmWW2xrUNizaSPHee+89/PLLL5g0adJT00Q4L5uIMjIysGjRIvj5+eH999+XnY5w27dvR2xsLLp06QJbW1sUFRXh+PHj8PX1xciRI2WnR0QK0KFDB9ja2qJt27baGQeVlZVQqVRmvRWKOWPRRooXERGhd4qT7pxtIqqdXF1dYWNjAwcHh6duTlJTUyVnZ3gjR45EVFQULC0ttecePXqEUaNGIS4uTmJmRKQUGo1G73s1bRVCysdGJKR4QUFBslMgIgXLysqSnYJRlZeXo6CgAE2bNtWeKygogIWFhcSsiEhJrl69qvc9Fm2miUUbKV5NLf/NeRSdiP43T+5HpMscp1CHhoZi8uTJAKCdHgkA8+bNk5kWESlIfHy83vfYkMQ0cXokERGZNH37tAHmPYX6/v37KCoqgp2dHWxtbWWnQ0QK8tNPP6F9+/ay0yADYtFGiuft7Y3hw4fD29sbjRo1kp0OEZFU6enp+OWXX9CnTx/MnTsX169fh6OjIxYsWMCbNCICAAQEBOD27dsYNmwYhgwZwv3+zACLNlK8mzdvQq1WY9euXWjZsiV8fHzg4eEhOy0iUojc3FysWLECq1atQr9+/VBeXo579+5h/fr16Ny5s+z0DM7X1xfr16/H7NmzMX36dHTo0AG//PILZs+ejdjYWNnpEZFC5OXlQa1WY/fu3WjVqhV8fHzQs2dP2WnRH1Tnt/8TIrmaNm2KgIAAxMfH48MPP0RaWhr8/PyeOSWKiGqPxYsXY8SIEQCAZs2aITU1FVFRUYiIiJCcmRiWlpZwdHRE3bp1tfuxmfu+dET0v2vSpAk++OAD7NixAx988AFSU1PRv39/2WnRH8RGJGRS3NzccPfuXRQXFyM1NdWs16sQ0e9TVFSEHj16AIB2bdcrr7yCBw8eyExLmN69e2PGjBl48cUXERgYCA8PDxw4cABdu3aVnRoRKUxFRQUyMjKgVqtx+fJl+Pn5yU6J/iAWbWQSzp07B7Varb0xGTFiBF577TXZaRGRApSWlmqPIyMjtce6+5iZkylTpuDo0aM4dOgQHB0dcfXqVfj6+qJv374AHk8p190OgIhqnzNnzkCtViM9PR09evTAqFGjzHK6eG3Coo0Uz8vLC05OThg+fDhmzJiB+vXry06JiBSkefPmOHz4cLW1GkeOHIGzs7PErMTq3r07unfvXuN7wcHB2LJli5EzIiIlCQ8Ph4+PD2bOnAlra2vZ6ZABsBEJKd6VK1fQsmVL2WkQkULl5eUhMDAQLVq0gIuLC3JycqDRaLBu3To4OjrKTs/o/P39ERUVJTsNIpJIo9E8tcdtFXMe0DJnLNpI8fz9/fVurs3RZCKqcvr0aWg0GjRr1gxdunTRe8Ni7saOHctrI1EtV3Xv9ORtfnFxMXbs2CEpK/ozOD2SFO/zzz+XnQIRKZharYa3tzc6duwIGxsbtG3bFgCwbt06fPTRR5KzIyIyPn1P24cPH27kTMhQ2PKfFK958+Y1/vn2229lp0ZECqA7ahwWFqY9PnLkiIx0pOMEGiLSp7bOQDAHLNrIZGVmZspOgYgUQLdI0Xdszh48eICHDx9qXw8ZMkRiNkREJAKnRxIRkUnTHTnWd2xOzp07h/nz52Pbtm3Yv38/FixYADs7O8yePRt9+/aFj4+P7BSJSLI5c+bUeF6j0Rg5EzIUFm2keMeOHXvqXGVlJYqKiiRkQ0RKk5eXh507d6KysrLa8a1bt2SnJsTKlSuxdOlSWFpaYvXq1fj73/+Oli1bIiAgQLtXGxHVbsOGDfufzpPysWgjxYuPj6/xfKdOnYycCREp0cCBA5Gbm/vUsZeXl8y0hKmsrMTLL7+Mmzdvori4GK+++ioAoE4drnggosfc3d1lp0AGxqKNFG/p0qWyUyAiBQsMDKz2+tq1aygrK4OLi4ukjMQqKytDRUUF9u7di169egEA7t+/jwcPHkjOjIiIRGHRRorXt29fvWtTUlNTjZwNESnNqVOnEBYWBmdnZwwYMACRkZGws7ODp6cnJk2aJDs9gxs0aBAGDx6M0tJSbNiwARcvXsSsWbMQEBAgOzUiIhKEm2uTyUlJScGKFSsQEBAAPz8/2ekQkWQ+Pj5YsWIFCgoKMHHiRKSlpcHa2hpjxowx261B7t+/D2tra1hYWODmzZu4ceMGOnToIDstIiIShE/ayGQUFhZi4cKFuHPnDjZv3gxnZ2fZKRGRAlhbW6NVq1Zo1aoV2rZtCxsbGwBA3brm+xV3/fp1ODo6okGDBoiPj0dJSQlcXFzg4OAgOzUiIhLAfL/RyKzs2bMHy5cvx4QJEzBy5EjZ6RCRguhOn7awsNAem+tEktWrVyMzMxPl5eVo3LgxXn75ZTz33HMICQnB+vXrZadHREQCsGgjxQsODsbhw4cRHByMFi1aVNsCoFu3bhIzIyIlOHXqFN566y1tm/+q4/z8fNmpCZGRkYHY2FiUlJRg8ODB+PLLLwEAycnJkjMjIiJRWLSR4llaWuLNN99EZmYmMjMzq73Hoo2Izpw588z309PT4enpaaRsxKtfvz4AwMrKCs2aNdOeLy8vl5USEREJxqKNFK+q5X95eTkKCgrQqFGjalOgiIieZcOGDWZVtN29exfHjh1DZWUl7t+/rz2+d++e7NSIiEgQFm2keOXl5QgPD0d6ejoaNWqE/Px8eHp6IiQkBJaWlrLTIyKFM7e1be3atUN8fDzKysrg5OSE2NhY1K1bF66urrJTIyIiQerIToDot6xZswZOTk5ISUnBtm3bkJKSAgcHB6xatUp2akRkAvTt82iqwsLCYG9vjzNnzuDevXs4efIkbG1tsWjRItmpERGRICzaSPEyMzOrbZBbp04dBAYG4vjx4xKzIiKSIyIiAo0bN0ZKSgpiYmLwww8/cCCLiMjMsWgjxdM3BdKc92AiIsMxt+mRmZmZmDhxova1SqXiQBYRkZlj0UaKZ29vj+zs7GrnsrKyYG9vLykjIlKawsJC7fHhw4erdZoNDQ2VkZIwHMgiIqp9eIUnxQsNDUVQUBB69eoFFxcXaDQapKWlYe3atbJTIyIFSEpKQkREBJKSkvD1119j3759aNq0KY4dO4apU6fCzc1NdooGVTWQ1aZNG+258+fPcyCLiMiMqSrNbd4ImZ1jx46huLgYxcXFyM3NRZMmTWBvbw87Ozvu00ZEGD9+PL766itYW1vD09MT8fHxcHR0xMiRIxETEyM7PYPLycnRO5DVqlUr2ekREZEAfNJGihcfH1/tdXZ2Nvbv34/y8nIcOXJEUlZEpBQWFhawtrZGdnY2nJyc4OTkBMD8ukZWcXFxQUxMDNLS0pCTk4M2bdpg8uTJsLW1lZ0aEREJwqKNFK9qc20AyMvLw/z589GxY0e2tyYiAI8bjVy7dg1xcXHaTbQvX75stkUbAFhZWaF///6y0yAiIiNhIxIyGWq1Gu+99x68vLywbt06NG7cWHZKRKQA06ZNQ1BQEK5evYqJEyfixIkTeP/99/Hpp5/KTo2IiMgguKaNFC8/Px8LFixAWVkZwsLC0KRJE9kpEZGCPXr0SHtcr149iZkQEREZBos2Urzu3bvDwsJCO+1Jl+7USSKqna5du4YtW7bAwcEB48ePh5WVFTIyMrBo0SIkJyfLTo+IiOhPY9FGiqe739KT3N3djZgJESnR6NGj8e6770Kj0aCkpAQqlQppaWlYuHAhXn/9ddnpERER/WlsREKKx8KMiJ5FpVLBx8cHANC3b1/06tULCQkJnBpJRERmg0UbERGZtDp1/ttTq1GjRggLC5OYDRERkeGxeyQREZk03db+3KuMiIjMEde0ERGRSevbt6+2cNP9SlOpVEhNTZWVFhERkcGwaCMiIrN04cIFtG3bVnYaREREfxrXtBERkdkoLS1FSkoKoqOjcevWLaSkpMhOiYiI6E9j0UZERCYvJycH0dHRSE5ORnl5OVasWMF2/0REZDZYtBERkUmbOHEiysrKMGjQICQmJmL69Oks2IiIyKyweyQREZk0Ozs7FBcXIy8vD/fu3avWTZKIiMgcsBEJERGZvMLCQiQmJiIhIQG5ubkIDQ1Fv379uAUAERGZBRZtRERkVrKysrBjxw7s2bMHaWlpstMhIiL601i0ERGRSdPX2n/v3r3o27evhIyIiIgMi2vaiIjIpIWFhWmPJ0yYoD3evHmzhGyIiIgMj0UbERGZNN0JI2VlZTWeJyIiMmUs2oiIyKTpdovUd0xERGTKuE8bERGZtJKSEly7dg0VFRV49OhRtWMiIiJzwEYkRERk0t5++204OzvXOB0yKipKQkZERESGxSdtRERk0iwsLDB8+HAMGTJEdipERERCcE0bERGZtJiYGBw6dAjTpk1DYWGh7HSIiIgMjtMjiYjILGRkZGDhwoXo0qWL9tzSpUslZkRERGQYnB5JREQmLzs7GxEREXB3d8fgwYNlp0NERGRQLNqIiMikRUREICkpCZ9++il69eolOx0iIiKDY9FGREQm7ebNm4iLi4OdnZ3sVIiIiITgmjYiIiIiIiIFY/dIIiIiIiIiBWPRRkREREREpGAs2oiIiIiIiBSMRRsREREREZGCsWgjIiIiIiJSMBZtRERERERECsaijYiIiIiISMH+H3Mkh4aU3NmkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x148c77f8208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs.identify_collinear(correlation_threshold=0.98)\n",
    "fs.plot_collinear()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "要查看阈值以上的相关细节，我们访问`record_collinear` 属性，它是一个DataFrame。  `drop_feature` 将被删除，并且对于每个将被删除的特征，它与 `corr_feature`可能存在多个相关性，而这些相关性都高于`correlation_threshold`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>corr_feature</th>\n",
       "      <th>corr_value</th>\n",
       "      <th>drop_feature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AMT_CREDIT</td>\n",
       "      <td>0.987232</td>\n",
       "      <td>AMT_GOODS_PRICE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DAYS_EMPLOYED</td>\n",
       "      <td>-0.999533</td>\n",
       "      <td>FLAG_EMP_PHONE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>YEARS_BUILD_AVG</td>\n",
       "      <td>0.992120</td>\n",
       "      <td>YEARS_BUILD_MODE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>COMMONAREA_AVG</td>\n",
       "      <td>0.988074</td>\n",
       "      <td>COMMONAREA_MODE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FLOORSMAX_AVG</td>\n",
       "      <td>0.984663</td>\n",
       "      <td>FLOORSMAX_MODE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      corr_feature  corr_value      drop_feature\n",
       "0       AMT_CREDIT    0.987232   AMT_GOODS_PRICE\n",
       "1    DAYS_EMPLOYED   -0.999533    FLAG_EMP_PHONE\n",
       "2  YEARS_BUILD_AVG    0.992120  YEARS_BUILD_MODE\n",
       "3   COMMONAREA_AVG    0.988074   COMMONAREA_MODE\n",
       "4    FLOORSMAX_AVG    0.984663    FLOORSMAX_MODE"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.record_collinear.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. 零重要特征\n",
    "此方法依赖于机器学习模型来识别要删除的特征。 因此，它是有标签的监督学习问题。 该方法通过使用LightGBM库中实现的梯度增强机找到特征重要性。\n",
    "\n",
    "为了减少所计算的特征重要性的差异，默认情况下对模型进行了10次训练。 默认情况下，还使用验证集（训练数据的15％）通过提前停止训练模型，以识别要训练的最优估计量。 可以将以下参数传递给`identify_zero_importance` 方法：\n",
    "\n",
    "* `task`: 可以是 `classification` 或 `regression`。 指标和标签必须与任务匹配。\n",
    "* `eval_metric`: 用于提前停止的度量（例如，用于分类的auc或用于回归的`l2` ）。 要查看可用指标的列表，请参阅LightGBM文档：(http://testlightgbm.readthedocs.io/en/latest/Parameters.html#metric-parameters)。\n",
    "* `n_iterations`: 训练次数。 特征重要性是在训练运行中平均得出的 (默认为10)。\n",
    "* `early_stopping`: 训练模型时是否使用提前停止（默认= True）。 当验证集的性能对于指定数量的估计量（此实现中默认为100）不再降低时，提早停止将停止训练估计量（决策树）。 早停是一种正则化形式，用于防止训练数据过拟合。\n",
    "\n",
    "首先对数据进行一次独热编码，以供模型使用。 这意味着某些零重要性特征可以通过一键编码来创建。 要查看单编码的列，我们可以访问 `FeatureSelector`的`one_hot_features` 。\n",
    "\n",
    "**注意**：与其他方法相比，模型的特征重要性是不确定的（具有少许随机性）。 每次运行此方法时，其结果都可能更改。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Gradient Boosting Model\n",
      "\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[72]\tvalid_0's binary_logloss: 0.254264\tvalid_0's auc: 0.746911\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[108]\tvalid_0's binary_logloss: 0.251856\tvalid_0's auc: 0.75226\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[18]\tvalid_0's binary_logloss: 0.258582\tvalid_0's auc: 0.747935\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[33]\tvalid_0's binary_logloss: 0.254186\tvalid_0's auc: 0.754061\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[9]\tvalid_0's binary_logloss: 0.266813\tvalid_0's auc: 0.722839\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[48]\tvalid_0's binary_logloss: 0.254795\tvalid_0's auc: 0.750713\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[5]\tvalid_0's binary_logloss: 0.269148\tvalid_0's auc: 0.743539\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[21]\tvalid_0's binary_logloss: 0.257916\tvalid_0's auc: 0.738772\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[58]\tvalid_0's binary_logloss: 0.245383\tvalid_0's auc: 0.782554\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[42]\tvalid_0's binary_logloss: 0.259282\tvalid_0's auc: 0.715016\n",
      "\n",
      "84 features with zero importance after one-hot encoding.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs.identify_zero_importance(task = 'classification', eval_metric = 'auc', \n",
    "                            n_iterations = 10, early_stopping = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "运行梯度提升模型需要对特征进行独热编码。 这些特征保存在 `FeatureSelector`的 `one_hot_features` 属性中。 原始特征保存在`base_features`中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 121 original features\n",
      "There are 134 one-hot features\n"
     ]
    }
   ],
   "source": [
    "one_hot_features = fs.one_hot_features\n",
    "base_features = fs.base_features\n",
    "print('There are %d original features' % len(base_features))\n",
    "print('There are %d one-hot features' % len(one_hot_features))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`FeatureSelector` 的 `data` 属性保存原始DataFrame。 独热编码后， `data_all`属性将保留原始数据以及独热编码特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME_CONTRACT_TYPE_Cash loans</th>\n",
       "      <th>NAME_CONTRACT_TYPE_Revolving loans</th>\n",
       "      <th>CODE_GENDER_F</th>\n",
       "      <th>CODE_GENDER_M</th>\n",
       "      <th>CODE_GENDER_XNA</th>\n",
       "      <th>FLAG_OWN_CAR_N</th>\n",
       "      <th>FLAG_OWN_CAR_Y</th>\n",
       "      <th>FLAG_OWN_REALTY_N</th>\n",
       "      <th>FLAG_OWN_REALTY_Y</th>\n",
       "      <th>NAME_TYPE_SUITE_Children</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 255 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   NAME_CONTRACT_TYPE_Cash loans  NAME_CONTRACT_TYPE_Revolving loans  \\\n",
       "0                              1                                   0   \n",
       "1                              0                                   1   \n",
       "2                              1                                   0   \n",
       "3                              1                                   0   \n",
       "4                              1                                   0   \n",
       "5                              1                                   0   \n",
       "6                              1                                   0   \n",
       "7                              1                                   0   \n",
       "8                              1                                   0   \n",
       "9                              1                                   0   \n",
       "\n",
       "   CODE_GENDER_F  CODE_GENDER_M  CODE_GENDER_XNA  FLAG_OWN_CAR_N  \\\n",
       "0              1              0                0               0   \n",
       "1              1              0                0               0   \n",
       "2              1              0                0               1   \n",
       "3              1              0                0               1   \n",
       "4              1              0                0               1   \n",
       "5              1              0                0               0   \n",
       "6              1              0                0               1   \n",
       "7              1              0                0               1   \n",
       "8              1              0                0               1   \n",
       "9              0              1                0               0   \n",
       "\n",
       "   FLAG_OWN_CAR_Y  FLAG_OWN_REALTY_N  FLAG_OWN_REALTY_Y  \\\n",
       "0               1                  1                  0   \n",
       "1               1                  0                  1   \n",
       "2               0                  0                  1   \n",
       "3               0                  0                  1   \n",
       "4               0                  0                  1   \n",
       "5               1                  1                  0   \n",
       "6               0                  0                  1   \n",
       "7               0                  0                  1   \n",
       "8               0                  0                  1   \n",
       "9               1                  0                  1   \n",
       "\n",
       "   NAME_TYPE_SUITE_Children             ...              FLAG_DOCUMENT_18  \\\n",
       "0                         0             ...                             0   \n",
       "1                         0             ...                             0   \n",
       "2                         0             ...                             0   \n",
       "3                         0             ...                             0   \n",
       "4                         0             ...                             0   \n",
       "5                         0             ...                             0   \n",
       "6                         0             ...                             0   \n",
       "7                         0             ...                             0   \n",
       "8                         0             ...                             0   \n",
       "9                         0             ...                             0   \n",
       "\n",
       "   FLAG_DOCUMENT_19  FLAG_DOCUMENT_20  FLAG_DOCUMENT_21  \\\n",
       "0                 0                 0                 0   \n",
       "1                 0                 0                 0   \n",
       "2                 0                 0                 0   \n",
       "3                 0                 0                 0   \n",
       "4                 0                 0                 0   \n",
       "5                 0                 0                 0   \n",
       "6                 0                 0                 0   \n",
       "7                 0                 0                 0   \n",
       "8                 0                 0                 0   \n",
       "9                 0                 0                 0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_HOUR  AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
       "0                         0.0                        0.0   \n",
       "1                         0.0                        0.0   \n",
       "2                         0.0                        0.0   \n",
       "3                         0.0                        0.0   \n",
       "4                         0.0                        0.0   \n",
       "5                         NaN                        NaN   \n",
       "6                         0.0                        0.0   \n",
       "7                         0.0                        0.0   \n",
       "8                         0.0                        0.0   \n",
       "9                         NaN                        NaN   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
       "0                         0.0                        0.0   \n",
       "1                         0.0                        0.0   \n",
       "2                         1.0                        0.0   \n",
       "3                         0.0                        0.0   \n",
       "4                         0.0                        0.0   \n",
       "5                         NaN                        NaN   \n",
       "6                         0.0                        1.0   \n",
       "7                         0.0                        0.0   \n",
       "8                         0.0                        0.0   \n",
       "9                         NaN                        NaN   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                        0.0                         1.0  \n",
       "1                        0.0                         0.0  \n",
       "2                        0.0                         7.0  \n",
       "3                        0.0                         1.0  \n",
       "4                        1.0                         1.0  \n",
       "5                        NaN                         NaN  \n",
       "6                        0.0                         0.0  \n",
       "7                        1.0                         0.0  \n",
       "8                        0.0                         0.0  \n",
       "9                        NaN                         NaN  \n",
       "\n",
       "[10 rows x 255 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.data_all.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们可以使用多种方法来检查特征重要性的结果。 首先，我们可以访问具有零重要性的特征列表。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ORGANIZATION_TYPE_Mobile',\n",
       " 'ORGANIZATION_TYPE_Military',\n",
       " 'ORGANIZATION_TYPE_Legal Services',\n",
       " 'FLAG_DOCUMENT_7',\n",
       " 'ORGANIZATION_TYPE_Trade: type 2']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zero_importance_features = fs.ops['zero_importance']\n",
    "zero_importance_features[10:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 画出特征重要性\n",
    "\n",
    "使用 `plot_feature_importances` 的特征重要性画图将向我们显示 `plot_n` 最重要的特征（按归一化比例将特征加总为1）。 它还向我们显示了累积特征重要性与特征数量之间的关系。\n",
    "\n",
    "当我们绘制特征重要性时，我们可以传递一个阈值，该阈值标识达到指定的累积特征重要性所需的特征数量。 例如，`threshold = 0.99`将告诉我们占总重要性的99％所需的特征数量。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x148c9c3bfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x148c761ef60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "115 features required for 0.99 of cumulative importance\n"
     ]
    }
   ],
   "source": [
    "fs.plot_feature_importances(threshold = 0.99, plot_n = 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在 `FeatureSelector`中的 `feature_importances` 属性中可以访问所有的特征重要性。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "      <th>normalized_importance</th>\n",
       "      <th>cumulative_importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EXT_SOURCE_2</td>\n",
       "      <td>116.8</td>\n",
       "      <td>0.094042</td>\n",
       "      <td>0.094042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EXT_SOURCE_3</td>\n",
       "      <td>108.9</td>\n",
       "      <td>0.087681</td>\n",
       "      <td>0.181723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EXT_SOURCE_1</td>\n",
       "      <td>72.7</td>\n",
       "      <td>0.058535</td>\n",
       "      <td>0.240258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DAYS_BIRTH</td>\n",
       "      <td>58.8</td>\n",
       "      <td>0.047343</td>\n",
       "      <td>0.287601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SK_ID_CURR</td>\n",
       "      <td>50.9</td>\n",
       "      <td>0.040982</td>\n",
       "      <td>0.328583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DAYS_REGISTRATION</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.040258</td>\n",
       "      <td>0.368841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DAYS_ID_PUBLISH</td>\n",
       "      <td>49.8</td>\n",
       "      <td>0.040097</td>\n",
       "      <td>0.408937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DAYS_LAST_PHONE_CHANGE</td>\n",
       "      <td>47.2</td>\n",
       "      <td>0.038003</td>\n",
       "      <td>0.446940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DAYS_EMPLOYED</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.037842</td>\n",
       "      <td>0.484783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AMT_INCOME_TOTAL</td>\n",
       "      <td>37.1</td>\n",
       "      <td>0.029871</td>\n",
       "      <td>0.514654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  feature  importance  normalized_importance  \\\n",
       "0            EXT_SOURCE_2       116.8               0.094042   \n",
       "1            EXT_SOURCE_3       108.9               0.087681   \n",
       "2            EXT_SOURCE_1        72.7               0.058535   \n",
       "3              DAYS_BIRTH        58.8               0.047343   \n",
       "4              SK_ID_CURR        50.9               0.040982   \n",
       "5       DAYS_REGISTRATION        50.0               0.040258   \n",
       "6         DAYS_ID_PUBLISH        49.8               0.040097   \n",
       "7  DAYS_LAST_PHONE_CHANGE        47.2               0.038003   \n",
       "8           DAYS_EMPLOYED        47.0               0.037842   \n",
       "9        AMT_INCOME_TOTAL        37.1               0.029871   \n",
       "\n",
       "   cumulative_importance  \n",
       "0               0.094042  \n",
       "1               0.181723  \n",
       "2               0.240258  \n",
       "3               0.287601  \n",
       "4               0.328583  \n",
       "5               0.368841  \n",
       "6               0.408937  \n",
       "7               0.446940  \n",
       "8               0.484783  \n",
       "9               0.514654  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.feature_importances.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们可以使用这些结果来仅选择“$n$”个最重要的特征。 例如，如果我们希望排名前100位的重要性最高，则可以执行以下操作。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hundred_features = list(fs.feature_importances.loc[:99, 'feature'])\n",
    "len(one_hundred_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. 低重要性特征\n",
    "此方法使用梯度提升算法（必须首先运行`identify_zero_importance`）通过查找达到指定的累积总特征重要性所需的最低特征重要性的特征，来构建特征重要性。 例如，如果我们输入0.99，则将找到最不重要的特征重要性，这些特征总的不到总特征重要性的99％。\n",
    "\n",
    "使用此方法时，我们必须已经运行了`identify_zero_importance` ，并且需要传递一个`cumulative_importance` ，该值占总特征重要性的一部分。\n",
    "\n",
    "**注意：**此方法建立在梯度提升模型的重要性基础之上，并且还是不确定的。 我建议使用不同的参数多次运行这两种方法，并测试每个结果的特征集，而不是只选择一个数字。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "114 features required for cumulative importance of 0.99 after one hot encoding.\n",
      "125 features do not contribute to cumulative importance of 0.99.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs.identify_low_importance(cumulative_importance = 0.99)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "要删除的低重要性特征是指那些对指定的累积重要性无贡献的特征。这些特征也可以在 `ops` 词典中找到。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['NAME_HOUSING_TYPE_With parents',\n",
       " 'ORGANIZATION_TYPE_Government',\n",
       " 'OCCUPATION_TYPE_Security staff',\n",
       " 'ELEVATORS_MODE',\n",
       " 'NAME_FAMILY_STATUS_Separated']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "low_importance_features = fs.ops['low_importance']\n",
    "low_importance_features[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 删除特征\n",
    "一旦确定了要删除的特征，便可以通过多种方式删除这些特征。 我们可以访问`removal_ops`词典中的任何功能列表，并手动删除列。 我们还可以使用 `remove` 方法，传入标识我们要删除的特征的方法。\n",
    "\n",
    "此方法返回结果数据，然后我们可以将其用于机器学习。 仍然可以在特征选择器的 `data` 属性中访问原始数据。\n",
    "\n",
    "请注意用于删除特征的方法！ 在使用删除特征之前，最好先检查将要`remove`的特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed 17 features.\n"
     ]
    }
   ],
   "source": [
    "train_no_missing = fs.remove(methods = ['missing'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed 101 features.\n"
     ]
    }
   ],
   "source": [
    "train_no_missing_zero = fs.remove(methods = ['missing', 'zero_importance'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "要从所有方法中删除特征，请传入 `method='all'`。 在执行此操作之前，我们可以使用`check_removal`检查将删除了多少个特征。 这将返回已被识别为要删除的所有特征的列表。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total of 150 features identified for removal\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['ORGANIZATION_TYPE_Industry: type 2',\n",
       " 'WALLSMATERIAL_MODE_Stone, brick',\n",
       " 'CODE_GENDER_XNA',\n",
       " 'AMT_REQ_CREDIT_BUREAU_DAY',\n",
       " 'ORGANIZATION_TYPE_Security Ministries',\n",
       " 'FONDKAPREMONT_MODE',\n",
       " 'FLAG_DOCUMENT_7',\n",
       " 'NAME_TYPE_SUITE_Other_B',\n",
       " 'ORGANIZATION_TYPE_Industry: type 13',\n",
       " 'NAME_CONTRACT_TYPE_Revolving loans',\n",
       " 'OCCUPATION_TYPE_Sales staff',\n",
       " 'OCCUPATION_TYPE_IT staff',\n",
       " 'ORGANIZATION_TYPE_Transport: type 3',\n",
       " 'NAME_TYPE_SUITE_Other_A',\n",
       " 'REG_REGION_NOT_LIVE_REGION']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_to_remove = fs.check_removal()\n",
    "all_to_remove[10:25]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在我们可以删除所有已识别的特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['missing', 'single_unique', 'collinear', 'zero_importance', 'low_importance'] methods have been run\n",
      "\n",
      "Removed 150 features.\n"
     ]
    }
   ],
   "source": [
    "train_removed = fs.remove(methods = 'all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 处理独热特征\n",
    "\n",
    "如果我们查看返回的DataFrame，可能会注意到原始数据中没有的几个新列。 这些是在对数据进行独热编码以进行机器学习时创建的。 要删除所有独热特征，我们可以将 `keep_one_hot = False` 传递给 `remove` 方法。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['missing', 'single_unique', 'collinear', 'zero_importance', 'low_importance'] methods have been run\n",
      "\n",
      "Removed 189 features including one-hot features.\n"
     ]
    }
   ],
   "source": [
    "train_removed_all = fs.remove(methods = 'all', keep_one_hot=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Number of Features 121\n",
      "Final Number of Features:  66\n"
     ]
    }
   ],
   "source": [
    "print('Original Number of Features', train.shape[1])\n",
    "print('Final Number of Features: ', train_removed_all.shape[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 使用所有方法的替代选项\n",
    "\n",
    "如果我们不想一次运行一个识别方法，则可以使用`identify_all` 在一次调用中运行所有方法。 对于此功能，我们需要传入参数字典以用于每种单独的识别方法。\n",
    "\n",
    "以下代码在一个调用中完成了上述步骤。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17 features with greater than 0.60 missing values.\n",
      "\n",
      "4 features with a single unique value.\n",
      "\n",
      "21 features with a correlation magnitude greater than 0.98.\n",
      "\n",
      "Training Gradient Boosting Model\n",
      "\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[100]\tvalid_0's binary_logloss: 0.251527\tvalid_0's auc: 0.747956\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[26]\tvalid_0's binary_logloss: 0.255572\tvalid_0's auc: 0.740053\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[92]\tvalid_0's binary_logloss: 0.242676\tvalid_0's auc: 0.778073\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[36]\tvalid_0's binary_logloss: 0.255766\tvalid_0's auc: 0.735214\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[39]\tvalid_0's binary_logloss: 0.258468\tvalid_0's auc: 0.724103\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[41]\tvalid_0's binary_logloss: 0.257077\tvalid_0's auc: 0.730639\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[55]\tvalid_0's binary_logloss: 0.243672\tvalid_0's auc: 0.779974\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[69]\tvalid_0's binary_logloss: 0.253203\tvalid_0's auc: 0.747206\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[79]\tvalid_0's binary_logloss: 0.24819\tvalid_0's auc: 0.761093\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[55]\tvalid_0's binary_logloss: 0.244782\tvalid_0's auc: 0.77629\n",
      "\n",
      "77 features with zero importance after one-hot encoding.\n",
      "\n",
      "115 features required for cumulative importance of 0.99 after one hot encoding.\n",
      "124 features do not contribute to cumulative importance of 0.99.\n",
      "\n",
      "151 total features out of 255 identified for removal after one-hot encoding.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs = FeatureSelector(data = train, labels = train_labels)\n",
    "\n",
    "fs.identify_all(selection_params = {'missing_threshold': 0.6, 'correlation_threshold': 0.98, \n",
    "                                    'task': 'classification', 'eval_metric': 'auc', \n",
    "                                     'cumulative_importance': 0.99})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['missing', 'single_unique', 'collinear', 'zero_importance', 'low_importance'] methods have been run\n",
      "\n",
      "Removed 151 features.\n"
     ]
    }
   ],
   "source": [
    "train_removed_all_once = fs.remove(methods = 'all', keep_one_hot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "      <th>normalized_importance</th>\n",
       "      <th>cumulative_importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EXT_SOURCE_2</td>\n",
       "      <td>166.1</td>\n",
       "      <td>0.093525</td>\n",
       "      <td>0.093525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EXT_SOURCE_3</td>\n",
       "      <td>147.4</td>\n",
       "      <td>0.082995</td>\n",
       "      <td>0.176520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EXT_SOURCE_1</td>\n",
       "      <td>97.4</td>\n",
       "      <td>0.054842</td>\n",
       "      <td>0.231363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DAYS_BIRTH</td>\n",
       "      <td>83.3</td>\n",
       "      <td>0.046903</td>\n",
       "      <td>0.278266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SK_ID_CURR</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.039977</td>\n",
       "      <td>0.318243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        feature  importance  normalized_importance  cumulative_importance\n",
       "0  EXT_SOURCE_2       166.1               0.093525               0.093525\n",
       "1  EXT_SOURCE_3       147.4               0.082995               0.176520\n",
       "2  EXT_SOURCE_1        97.4               0.054842               0.231363\n",
       "3    DAYS_BIRTH        83.3               0.046903               0.278266\n",
       "4    SK_ID_CURR        71.0               0.039977               0.318243"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fs.feature_importances.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于特征重要性已更改，因此删除的特征数略有差异。由缺失的（`missing`）、单一的（`single_unique`）和共线（ `collinear`）确定要删除的特征数量将保持不变，因为它们是确定性的，但是由于多次训练模型，零重要性（ `zero_importance` ）和低重要性（`low_importance` ）的特征数量可能会有所不同。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 结论\n",
    "\n",
    "本笔记本演示了如何使用`FeatureSelector`类从数据集中删除特征。此实现中有几个重要注意事项：\n",
    "- 在机器学习模型的多次运行中，特征重要性将发生变化。\n",
    "- 决定是否保留从一个独热编码创建的额外特征。\n",
    "- 为不同的参数尝试几个不同的值，以确定哪些参数最适合机器学习任务。\n",
    "- 对于相同的参数，缺失的（`missing`）、单一的（`single_unique`）和共线（ `collinear`）的输出将保持不变。\n",
    "- 特征选择是机器学习工作流中的一个关键步骤，它可能需要多次迭代来优化。\n",
    "我很感激你对这个项目的任何评论、反馈或帮助。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 参考\n",
    "Jupyter notebook：\n",
    "\n",
    "https://github.com/WillKoehrsen/feature-selector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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